Mekr is an On-Demand manufacturing platform for the electronics industry.

Mekr - Crunchbase Company Profile & Funding

Information on valuation, funding, cap tables, investors, and executives for Mekr Technologies. Use the PitchBook Platform to explore the full profile.

Mekr Technologies Company Profile: Valuation & Investors | PitchBook

Mekr Technologies is a one-stop end-to-end electronics product manufacturing platform that offers top notch quality, assured lead time and competitive price.

Electronics Manufacturing Platform | Mekr Technologies

🌟🙏🏹 🌐 Visit our website: https://www.mekr.in/ #HappyDussehra #FestivalOfVictory #GoodOverEvil #manufacturing #homeappliance #b2b #startup #factory #egg #makeinindia #manufacture #india #electronic #pcb ... In the mid-1900s, Paul Eisler was working at a music printing company in Austria.

MEKR | LinkedIn

&quot;Mekr Technologies is a digital manufacturing platform that provides a one stop manufacturing solution for product companies and ensures a steady business for its manufacturing partners. They provide the best prices, top quality and reliable lead times for manufacturing services.

Mekr Company Profile, information, investors, valuation & Funding

On-Demand manufacturing platform for electronics industry

Mekr - Funding, Financials, Valuation & Investors

MEKR´S s.r.o. is a Czech private company (without foreign capital participation), a wholesaler of fasteners and anchor equipment. With its warehouse located in Úpice, and a network of dealers, the company provides services to important business partners and customers from various industries, ...

MEKR'S – Spolehlivý dodavatel spojovacího materiálu

MEKR TECHNOLOGIES PRIVATE LIMITED - company information, business information and directors/partners details

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MEKR&#x27;S s.r.o. is a Czech private company, wholesaler of fasteners and anchoring material. We provide services to business partners and customers from various industries, such as mechanical and electrical engineering, metallurgy, and the automobile industry. We offer more than 40,000 articles ...

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Mekr Technologies Private Limited North West Delhi, Delhi detailed registration information. Get Mekr Technologies Pvt. Ltd. company (U72900DL2022PTC392949) services and products as well as Mekr Technologies Pvt. Ltd. legal details

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Mekr Technologies is a digital manufacturing platform that provides a one stop manufacturing solution for product companies and ensures a steady business for its manufacturing partners. We provide the best prices, top quality and reliable lead times for manufacturing services.

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Up-to-date verified information: address, phone, e-mail, operating hours and reviews MEKR&#x27;S, s.r.o., Úpice. Phone: 499 300 …

MEKR'S, s.r.o. (Úpice) • Firmy.cz

Reliable supplier of a wide selection of fasteners and anchoring technology, operating on the market since 1994. Over 40.000 stock items of quality fastening material conforming to DIN and ISO standards. We are also able to satisfy orders in accordance with individual customer specifications ...

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Mekris in the Manufacturing Tech Industry

Mekr

Authors:
(1) Aya Kherrour, Department of Information Engineering and Computer Science University of Trento;
(2) Marco Robol, Department of Information Engineering and Computer Science University of Trento;
(3) Marco Roveri, Department of Information Engineering and Computer Science University of Trento;
(4) Paolo Giorgini, Department of Information Engineering and Computer Science University of Trento. 3 Experimental environment and performance metrices Heuristic search algorithms play an important role in fields such as robotic pathfinding, as they determine the optimal path given a starting position and a goal position. Grid-based environments are commonly used for representing real-world environment scenarios, where these algorithms can be implemented, such as in autonomous navigation and robotics [6]. In this study, we comprehensively analyze well known heuristic search algorithms, namely D*, D* Lite, LPA*, LRTA*, RTAA*, and ARA* in different grid environments. We use the Euclidean distance heuristic to guide the search of the algorithms and assess the impact of a few grid characteristics, such as the obstacle density and the grid size, on the performance of the algorithms. In our study, we use grid-based environments due to their simplicity, and control ease, in addition to being commonly used in path-planning tasks in the research community. The grids are composed of white cells, representing traversable states, whereas the black ones represent non-traversable obstacles. The agent in our simulation can move in eight directions, with a cost equal to 1 for horizontal and vertical moves, and √ 2 for diagonal movements. We used two types of grid environments: randomly generated grid environments and personalized grid environments. Randomly generated grid-based environments: The grids are characterized by three parameters: grid size (NxN), obstacle density, and the distance between the start and the goal states. To investigate the impact of each parameter on the performance of the search algorithms, we varied each parameter independently while keeping the other parameters constant. The variations included varying the grid size, varying start to goal distance, and varying the obstacle densities. For each grid in a variation, we generated ten random instances of the same grid parameters (e.g., a grid with 0.25 of obstacle density, size 300x300, and 140 of start to goal distance, have ten instances, which were generated randomly). Personalised grid environment: Designed for simulating more specific scenarios. These grids have fixed size (71x31 units) and a fixed position for both the start and goal states, and they were divided into two parts based on their obstacle configuration: • Horizontal wall configuration: For these experiments, we add horizontal walls of half grid width every 10 units of grid length. We added the walls in two orientations: once from left to right, and once from right to left in the newly generated grid. • Horizontal wall length configuration: Here, we add all possible walls that can be placed within the grid length, and each time we generate a new grid we increase all wall lengths by 2 units. We adopted these two distinct environments to provide a thorough analysis, seeking to reveal nuanced insights into the performance of the search algorithm in the presence of two different hindering scenarios. In the first one, obstacles are scattered randomly within the grid, whereas in the second one, the wall-like structures, appear as a mass of connected obstacles. To evaluate the performance of the different search algorithms used in the experiments, we have selected the following metrics: • Path cost: The metric measures the path length or the number of executed actions from the start to the goal state. It indicates the quality of the solution. • Memory consumption: It measures the required amount of memory for the algorithm to find a solution. It is relevant to check the scalability of the algorithm, and it is measured in (KB). • Solving Time: Represent the total time an algorithm takes to find a solution in (ms), measured in milliseconds (ms). We carried out our experiments on 3.30GHz 27 Intel i9 cores, equipped with 250Gb of RAM and running Ubuntu Linux 22.04. Using the following settings: All algorithms were using the Euclidean distance as the heuristic function. For LRTA* and RTAA*, we set the number of expended nodes to 250 for each iteration. The ARA* algorithm was run with a weight of 2.5 for the heuristic. We ran each algorithm 100 times on each grid to account for randomness and to ensure the reliability of the results. This paper is available on arxiv under CC 4.0 license. Authors: (1) Aya Kherrour, Department of Information Engineering and Computer Science University of Trento; (2) Marco Robol, Department of Information Engineering and Computer Science University of Trento; (3) Marco Roveri, Department of Information Engineering and Computer Science University of Trento; (4) Paolo Giorgini, Department of Information Engineering and Computer Science University of Trento. Authors: (1) Aya Kherrour, Department of Information Engineering and Computer Science University of Trento; (2) Marco Robol, Department of Information Engineering and Computer Science University of Trento; (3) Marco Roveri, Department of Information Engineering and Computer Science University of Trento; (4) Paolo Giorgini, Department of Information Engineering and Computer Science University of Trento. 3 Experimental environment and performance metrices Heuristic search algorithms play an important role in fields such as robotic pathfinding, as they determine the optimal path given a starting position and a goal position. Grid-based environments are commonly used for representing real-world environment scenarios, where these algorithms can be implemented, such as in autonomous navigation and robotics [6]. In this study, we comprehensively analyze well known heuristic search algorithms, namely D*, D* Lite, LPA*, LRTA*, RTAA*, and ARA* in different grid environments. We use the Euclidean distance heuristic to guide the search of the algorithms and assess the impact of a few grid characteristics, such as the obstacle density and the grid size, on the performance of the algorithms. In our study, we use grid-based environments due to their simplicity, and control ease, in addition to being commonly used in path-planning tasks in the research community. The grids are composed of white cells, representing traversable states, whereas the black ones represent non-traversable obstacles. The agent in our simulation can move in eight directions, with a cost equal to 1 for horizontal and vertical moves, and √ 2 for diagonal movements. We used two types of grid environments: randomly generated grid environments and personalized grid environments. Randomly generated grid-based environments: The grids are characterized by three parameters: Randomly generated grid-based environments: grid size (NxN), obstacle density, and the distance between the start and the goal states. To investigate the impact of each parameter on the performance of the search algorithms, we varied each parameter independently while keeping the other parameters constant. The variations included varying the grid size, varying start to goal distance, and varying the obstacle densities. For each grid in a variation, we generated ten random instances of the same grid parameters (e.g., a grid with 0.25 of obstacle density, size 300x300, and 140 of start to goal distance, have ten instances, which were generated randomly). Personalised grid environment: Designed for simulating more specific scenarios. These grids have fixed size (71x31 units) and a fixed position for both the start and goal states, and they were divided into two parts based on their obstacle configuration: Personalised grid environment: • Horizontal wall configuration: For these experiments, we add horizontal walls of half grid width every 10 units of grid length. We added the walls in two orientations: once from left to right, and once from right to left in the newly generated grid. Horizontal wall configuration: • Horizontal wall length configuration: Here, we add all possible walls that can be placed within Horizontal wall length configuration: the grid length, and each time we generate a new grid we increase all wall lengths by 2 units. We adopted these two distinct environments to provide a thorough analysis, seeking to reveal nuanced insights into the performance of the search algorithm in the presence of two different hindering scenarios. In the first one, obstacles are scattered randomly within the grid, whereas in the second one, the wall-like structures, appear as a mass of connected obstacles. To evaluate the performance of the different search algorithms used in the experiments, we have selected the following metrics: • Path cost: The metric measures the path length or the number of executed actions from the start to the goal state. It indicates the quality of the solution. Path cost: • Memory consumption: It measures the required amount of memory for the algorithm to find a solution. It is relevant to check the scalability of the algorithm, and it is measured in (KB). Memory consumption: • Solving Time: Represent the total time an algorithm takes to find a solution in (ms), measured in milliseconds (ms). Solving Time: We carried out our experiments on 3.30GHz 27 Intel i9 cores, equipped with 250Gb of RAM and running Ubuntu Linux 22.04. Using the following settings: All algorithms were using the Euclidean distance as the heuristic function. For LRTA* and RTAA*, we set the number of expended nodes to 250 for each iteration. The ARA* algorithm was run with a weight of 2.5 for the heuristic. We ran each algorithm 100 times on each grid to account for randomness and to ensure the reliability of the results. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

Performance Metrics of Heuristic Search Algorithms in Grid Environments

গ্রিড এনভায়রনমেন্টে হিউরিস্টিক সার্চ অ্যালগরিদমের পারফরম্যান্স মেট্রিক্স

Leistungsmetriken heuristischer Suchalgorithmen in Grid-Umgebungen

Métricas de rendimiento de algoritmos de búsqueda heurística en entornos de red

Mesures de performance des algorithmes de recherche heuristiques dans les environnements de grille

ग्रिड वातावरण में ह्यूरिस्टिक खोज एल्गोरिदम के प्रदर्शन मेट्रिक्स

グリッド環境におけるヒューリスティック探索アルゴリズムのパフォーマンス指標

그리드 환경에서 휴리스틱 검색 알고리즘의 성능 지표

Métricas de desempenho de algoritmos de pesquisa heurística em ambientes de grade

Метрики производительности алгоритмов эвристического поиска в грид-средах

Izgara Ortamlarında Sezgisel Arama Algoritmalarının Performans Ölçümleri

Số liệu hiệu suất của thuật toán tìm kiếm heuristic trong môi trường lưới

网格环境中启发式搜索算法的性能指标

Authors:
(1) Aya Kherrour, Department of Information Engineering and Computer Science University of Trento;
(2) Marco Robol, Department of Information Engineering and Computer Science University of Trento;
(3) Marco Roveri, Department of Information Engineering and Computer Science University of Trento;
(4) Paolo Giorgini, Department of Information Engineering and Computer Science University of Trento. The paper presents a comprehensive performance evaluation of some heuristic search algorithms in the context of autonomous systems and robotics. The objective of the study is to evaluate and compare the performance of different search algorithms in different problem settings on the pathfinding domain. Experiments give us insight into the behavior of the evaluated heuristic search algorithms, over the variation of different parameters: domain size, obstacle density, and distance between the start and the goal states. Results are then used to design a selection algorithm that, on the basis of problem characteristics, suggests the best search algorithm to use. 1 Introduction Autonomous agents and robotics have increasingly been used in various domains, such as, industrial applications [16, 18, 8], surveillance [17], and exploration [4]. These systems are designed to autonomously make decisions and execute actions based on their dynamic and unpredictable environments. Under such conditions, systems are required to be as reactive as possible to changes in the environment. Therefore, ensuring good performance is a significant challenge. In the case of planning, selecting the most effective search algorithm becomes crucial to enhance the overall performance of the system. Real-time heuristic search (RTS) (e.g.,[14, 9]) is a state-of-the-art approach for planning while executing, that helps minimize agent reaction time. These algorithms enable agents to make decisions by interleaving planning with execution while considering the evolving environment, which is an essential property in applications such as robotics, and video game agents. Despite the numerous methods proposed in this field [14, 13, 12, 3, 2], a comprehensive understanding of these algorithms remains elusive. Existing studies [1, 13, 10] have primarily focused on testing the performance of the algorithm based on a single parameter (e.g., look-ahead, sensor range). However, the influence of the problem domain characteristics on algorithm performance remains an understudied aspect. Our objective is to investigate the characteristics of the problem that may impact algorithm performances. To achieve this, we first review existing state of art of search algorithms and their applications. Then we design our experiments to evaluate some of the algorithm performances, also defining relevant metrics to use in the evaluation. Later on, experiment results are analyzed to provide a comprehensive understanding of how problem characteristics influence the performance of the different search algorithms. Finally, from the insight gained from our study, we introduce a selection algorithm that helps us to select the appropriate search algorithm. This paper is structured as follows. We begin by reviewing state-of-the-art search algorithms and previous performance evaluation studies. Next, we describe the approach we adopted to evaluate the search algorithms that are subject to our study, define the problem domain and its characteristics, and the performance metrics. In section four, we present our experimental results. Section five introduces our proposed selection algorithm with an execution example. Finally, we derive our conclusion, the limitations of our study, and possible directions for future work. This paper is available on arxiv under CC 4.0 license. Authors: (1) Aya Kherrour, Department of Information Engineering and Computer Science University of Trento; (2) Marco Robol, Department of Information Engineering and Computer Science University of Trento; (3) Marco Roveri, Department of Information Engineering and Computer Science University of Trento; (4) Paolo Giorgini, Department of Information Engineering and Computer Science University of Trento. Authors: (1) Aya Kherrour, Department of Information Engineering and Computer Science University of Trento; (2) Marco Robol, Department of Information Engineering and Computer Science University of Trento; (3) Marco Roveri, Department of Information Engineering and Computer Science University of Trento; (4) Paolo Giorgini, Department of Information Engineering and Computer Science University of Trento. The paper presents a comprehensive performance evaluation of some heuristic search algorithms in the context of autonomous systems and robotics. The objective of the study is to evaluate and compare the performance of different search algorithms in different problem settings on the pathfinding domain. Experiments give us insight into the behavior of the evaluated heuristic search algorithms, over the variation of different parameters: domain size, obstacle density, and distance between the start and the goal states. Results are then used to design a selection algorithm that, on the basis of problem characteristics, suggests the best search algorithm to use. 1 Introduction Autonomous agents and robotics have increasingly been used in various domains, such as, industrial applications [16, 18, 8], surveillance [17], and exploration [4]. These systems are designed to autonomously make decisions and execute actions based on their dynamic and unpredictable environments. Under such conditions, systems are required to be as reactive as possible to changes in the environment. Therefore, ensuring good performance is a significant challenge. In the case of planning, selecting the most effective search algorithm becomes crucial to enhance the overall performance of the system. Real-time heuristic search (RTS) (e.g.,[14, 9]) is a state-of-the-art approach for planning while executing, that helps minimize agent reaction time. These algorithms enable agents to make decisions by interleaving planning with execution while considering the evolving environment, which is an essential property in applications such as robotics, and video game agents. Despite the numerous methods proposed in this field [14, 13, 12, 3, 2], a comprehensive understanding of these algorithms remains elusive. Existing studies [1, 13, 10] have primarily focused on testing the performance of the algorithm based on a single parameter (e.g., look-ahead, sensor range). However, the influence of the problem domain characteristics on algorithm performance remains an understudied aspect. Our objective is to investigate the characteristics of the problem that may impact algorithm performances. To achieve this, we first review existing state of art of search algorithms and their applications. Then we design our experiments to evaluate some of the algorithm performances, also defining relevant metrics to use in the evaluation. Later on, experiment results are analyzed to provide a comprehensive understanding of how problem characteristics influence the performance of the different search algorithms. Finally, from the insight gained from our study, we introduce a selection algorithm that helps us to select the appropriate search algorithm. This paper is structured as follows. We begin by reviewing state-of-the-art search algorithms and previous performance evaluation studies. Next, we describe the approach we adopted to evaluate the search algorithms that are subject to our study, define the problem domain and its characteristics, and the performance metrics. In section four, we present our experimental results. Section five introduces our proposed selection algorithm with an execution example. Finally, we derive our conclusion, the limitations of our study, and possible directions for future work. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

Evaluating Heuristic Search Algorithms in Pathfinding: A Comprehensive Study on Performance Metrics 

**BERMUDA, Hamilton, April 17th, 2024/Chainwire/--**HashKey Global, the flagship global digital asset exchanges under HashKey Group, is thrilled to unveil the initial listing of Merlin Chain (MERL) Token. In collaboration with the Merlin Chain community, HashKey Global is launching a campaign with a prize pool of 200,000 MERL tokens. Participants have the chance to earn 50 MERL tokens by completing the 10,000 USDT trading volume task, coinciding with the Genesis Trading Campaign where users can earn HSK rewards based on their trading volume. Listing Timeline: MERL deposit opens: April 18, 2024, 10:00 (UTC)
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MERL withdrawal opens: April 20, 2024, 10:00 (UTC) Merlin With HashKey The listing of Merlin Chain's MERL Token on HashKey Global injects significant liquidity and visibility into Merlin's growth trajectory, enabling users to directly engage with this innovative ecosystem. As a trading platform, HashKey Global is more than just a marketplace; it also acts as an accelerator for technology and asset promotion. The partnership between Merlin Chain and HashKey Global not only enhances the Bitcoin ecosystem with innovative L2 solutions but also presents new investment opportunities in the crypto market. As these synergies continue to unfold, we can expect further innovations and value creation. Merlin Chain: Dynamic Bitcoin Layer2 Ecosystem Merlin Chain is a Bitcoin-native layer-2 solution that aims to add value to the ecosystem. It uses ZK-rollup technology to boost transaction scalability and employs a decentralized oracle network and on-chain fraud-proofs to interact with the Bitcoin network and verify uploaded transactions. Merlin Chain will bring innovation and utilities on Layer2 with those native Layer1 assets. With the listing of Merlin Chain (MERL) tokens on HashKey Global, both are poised to embark on an exciting journey of growth and collaboration, bringing enhanced liquidity, accessibility, and innovation to the digital asset ecosystem. Users are invited to join and unlock the potential of blockchain technology to revolutionize the future of finance together. About HashKey Global HashKey Global is one of the flagship global digital asset exchanges under HashKey Group, offering licensed digital asset trading services to users worldwide. HashKey Global has obtained a license from the Bermuda Monetary Authority with the potential to provide mainstream trading and service products such as spot, LaunchPad, contracts, leverage, and staking. HashKey Global does not operate nor onboard clients from the United States, Mainland China, Hong Kong SAR, and certain restricted countries or regions. More info about HashKey Global on global.hashkey.com. Uses can follow HashKey Global on Twitter and Discord About Merlin Chain (MERL) Merlin Chain is a native Bitcoin Layer2 committed to empowering Bitcoin's native assets, protocols, and products on Layer1 through its Layer2 network. The underlying infrastructure of Merlin Chain is built on Polygon's ZK technology, with a decentralized Oracle network for data availability, powered by Lumoz's decentralized ZK computing power network for ZKP computation. It allows challengers to present fraud proofs on disputed matters, utilizing Bitcoin's robust consensus mechanism to ensure the security of the Merlin Chain network. Website: https://merlinchain.io Twitter (X): https://twitter.com/MerlinLayer2 Telegram: https://t.me/merlin_chain Disclaimer: HashKey Global is a digital asset trading platform operated by HashKey Bermuda Limited under a Type F license granted by the Bermuda Monetary Authority. This information does not constitute an offer, solicitation, or recommendation for any investment product. Investing and trading virtual assets involve risks. HashKey Global does not service users from Hong Kong, United States, Mainland China and certain other jurisdictions in compliance with laws and regulations. Certain services, features, and campaigns may not be available in your jurisdiction. RISK WARNING ABOUT HSK: Users should be aware that HSK is not currently listed on any exchange and there is no guarantee that it will be listed in the future. As a result, HSK presently has no established market value. The timeline for the potential listing of HSK remains uncertain. In the event that HSK is successfully listed, it may be subject to various trading restrictions in accordance with applicable regulations and laws. These restrictions may include, but are not limited to, limiting the trading of HSK solely to eligible professional investors in select locations and subject to regulatory approval. The value of HSK is subject to substantial risk and may diminish or fluctuate significantly in response to various market conditions and other factors beyond HashKey's control. HashKey Global and its affiliates make no warranties, express or implied, in relation to HSK or any rewards and disclaims any liability relating thereto. Contact Luna Wang marketing@hashkey.com This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here. **BERMUDA, Hamilton, April 17th, 2024/Chainwire/--**HashKey Global, the flagship global digital asset exchanges under HashKey Group, is thrilled to unveil the initial listing of Merlin Chain (MERL) Token. In collaboration with the Merlin Chain community, HashKey Global is launching a campaign with a prize pool of 200,000 MERL tokens. Participants have the chance to earn 50 MERL tokens by completing the 10,000 USDT trading volume task, coinciding with the Genesis Trading Campaign where users can earn HSK rewards based on their trading volume. Listing Timeline: MERL deposit opens: April 18, 2024, 10:00 (UTC) MERL/USDT spot trading: April 19, 2024, 10:00 (UTC) MERL withdrawal opens: April 20, 2024, 10:00 (UTC) MERL deposit opens: April 18, 2024, 10:00 (UTC) MERL/USDT spot trading: April 19, 2024, 10:00 (UTC) MERL withdrawal opens: April 20, 2024, 10:00 (UTC) Merlin With HashKey The listing of Merlin Chain's MERL Token on HashKey Global injects significant liquidity and visibility into Merlin's growth trajectory, enabling users to directly engage with this innovative ecosystem. HashKey Global HashKey Global As a trading platform, HashKey Global is more than just a marketplace; it also acts as an accelerator for technology and asset promotion. The partnership between Merlin Chain and HashKey Global not only enhances the Bitcoin ecosystem with innovative L2 solutions but also presents new investment opportunities in the crypto market. As these synergies continue to unfold, we can expect further innovations and value creation. HashKey Global HashKey Global Merlin Chain: Dynamic Bitcoin Layer2 Ecosystem Merlin Chain is a Bitcoin-native layer-2 solution that aims to add value to the ecosystem. It uses ZK-rollup technology to boost transaction scalability and employs a decentralized oracle network and on-chain fraud-proofs to interact with the Bitcoin network and verify uploaded transactions. Merlin Chain will bring innovation and utilities on Layer2 with those native Layer1 assets. With the listing of Merlin Chain (MERL) tokens on HashKey Global, both are poised to embark on an exciting journey of growth and collaboration, bringing enhanced liquidity, accessibility, and innovation to the digital asset ecosystem. Users are invited to join and unlock the potential of blockchain technology to revolutionize the future of finance together. About HashKey Global HashKey Global is one of the flagship global digital asset exchanges under HashKey Group, offering licensed digital asset trading services to users worldwide. HashKey Global has obtained a license from the Bermuda Monetary Authority with the potential to provide mainstream trading and service products such as spot, LaunchPad, contracts, leverage, and staking. HashKey Global does not operate nor onboard clients from the United States, Mainland China, Hong Kong SAR, and certain restricted countries or regions. More info about HashKey Global on global.hashkey.com . Uses can follow HashKey Global on Twitter and Discord global.hashkey.com global.hashkey.com Twitter Twitter Discord Discord About Merlin Chain (MERL) Merlin Chain is a native Bitcoin Layer2 committed to empowering Bitcoin's native assets, protocols, and products on Layer1 through its Layer2 network. The underlying infrastructure of Merlin Chain is built on Polygon's ZK technology, with a decentralized Oracle network for data availability, powered by Lumoz's decentralized ZK computing power network for ZKP computation. It allows challengers to present fraud proofs on disputed matters, utilizing Bitcoin's robust consensus mechanism to ensure the security of the Merlin Chain network. Website: https://merlinchain.io https://merlinchain.io https://merlinchain.io Twitter (X): https://twitter.com/MerlinLayer2 https://twitter.com/MerlinLayer2 https://twitter.com/MerlinLayer2 Telegram: https://t.me/merlin_chain https://t.me/merlin_chain https://t.me/merlin_chain Disclaimer: HashKey Global is a digital asset trading platform operated by HashKey Bermuda Limited under a Type F license granted by the Bermuda Monetary Authority. This information does not constitute an offer, solicitation, or recommendation for any investment product. Investing and trading virtual assets involve risks. HashKey Global does not service users from Hong Kong, United States, Mainland China and certain other jurisdictions in compliance with laws and regulations. Certain services, features, and campaigns may not be available in your jurisdiction. RISK WARNING ABOUT HSK: Users should be aware that HSK is not currently listed on any exchange and there is no guarantee that it will be listed in the future. As a result, HSK presently has no established market value. The timeline for the potential listing of HSK remains uncertain. In the event that HSK is successfully listed, it may be subject to various trading restrictions in accordance with applicable regulations and laws. These restrictions may include, but are not limited to, limiting the trading of HSK solely to eligible professional investors in select locations and subject to regulatory approval. The value of HSK is subject to substantial risk and may diminish or fluctuate significantly in response to various market conditions and other factors beyond HashKey's control. HashKey Global and its affiliates make no warranties, express or implied, in relation to HSK or any rewards and disclaims any liability relating thereto. Contact Luna Wang marketing@hashkey.com This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here. This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here. here. here.

HashKey Global Announces Listing Of MERL Token With 200,000 MERL Prize Pool Campaign

Merit Circle's Q1 2024 treasury report unveiled a robust financial growth of over $94 million, with a total asset accumulation of $201 million. Beyond only making financial investments, Merit Circle wants to utilise blockchain technology to transform the gaming industry. The attempt to combine gaming with blockchain technology is a creative method to provide a more immersive and captivating gaming experience and to trigger a paradigm change in the industry via its gaming ecosystem, Beam, which is spread throughout several networks, such as Polygon, Immutable, and Avalanche. Gaming and blockchain technology together have the power to completely change how people play and communicate in virtual worlds. In addition to improving gameplay, the goal of blockchain integration is to establish a long-term paradigm for the gaming economy in which users may actually own a stake in the ecosystems they contribute to. This integration promises to improve the gaming experience by introducing new levels of player ownership and economic models, potentially changing how players interact with games. For instance, users of blockchain technology may own and exchange digital assets called Axies in the game Axie Infinity. This gives players a sense of control and ownership over the in-game items they possess, as well as a monetary value for the time and effort they invest in the game. Unveiling Robust Growth: Treasury Report Q1 2024 A compelling financial tale is presented in the treasury report for the first quarter of 2024 that is associated with the Merit Circle ecosystem. The information indicates that the overall assets of the ecosystem increased by more than $94 million, reaching a total of $201 million. This large gain is more than just a figure; it reflects the excellent strategic decision that Merit Circle made to join in the gaming sector, which is known for its rapid speed. As a very profitable enterprise, Saga stands out among significant investments as a noteworthy investment. In the beginning, Saga was valued at $125 million when it acquired Merit Circle; however, the price of the company has since increased to almost $5 billion after it has been completely diluted. This milestone not only demonstrates the possibility for strategic early-stage engagement in high-growth enterprises, but it also demonstrates the additional financial rewards that may be gained via investing and gaining attention on a new front as a result of its active participation in game production, notably with the online party game Forgotten Playland. The Treasury Department had around $30 million in $FP tokens at the end of the first quarter, which indicates that this game, which is scheduled to be published in April 2024, has already had a substantial influence on the financial situation. This game production initiative, which goes beyond basic investment to actively impact the gaming environment with cutting-edge goods, has resulted in a significant expansion of Merit Circle's operational reach, which has seen a significant increase in its operational reach. The strategy that is being taken is reflective of a more widespread trend in the gaming business, which is that strategic expenditures are increasingly propelling the company forward. The dedication of Merit Circle to openness, which can be seen in the regular publication of treasury reports, is the source of the high industry standard that the company has established. In this way, stakeholders are provided with a comprehensive perspective of the organization's financial status as well as its strategic goals, which in turn stimulates trust. Final Thoughts Merit Circle's strategic expansion and inventive push is something that I believe to be both fascinating and instructive. In addition to demonstrating that the Treasury is in excellent financial condition, the report for the first quarter of 2024 also demonstrates that the Treasury has a distinct strategic vision that has the potential to influence the direction that the entertainment industry will take in the years to come. It is the belief of Merit Circle that the seamless incorporation of blockchain technology into gaming has the potential to usher in a new era in which gaming is more than just a platform for economic involvement and progress; rather, it is also an experience that is pleasant. Don’t forget to like and share the story! Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR. Merit Circle's Q1 2024 treasury report unveiled a robust financial growth of over $94 million, with a total asset accumulation of $201 million. Merit Circle's Q1 2024 treasury report unveiled a robust financial growth of over $94 million, with a total asset accumulation of $201 million. Beyond only making financial investments, Merit Circle wants to utilise blockchain technology to transform the gaming industry. The attempt to combine gaming with blockchain technology is a creative method to provide a more immersive and captivating gaming experience and to trigger a paradigm change in the industry via its gaming ecosystem, Beam, which is spread throughout several networks, such as Polygon, Immutable, and Avalanche. Gaming and blockchain technology together have the power to completely change how people play and communicate in virtual worlds. Merit Circle In addition to improving gameplay, the goal of blockchain integration is to establish a long-term paradigm for the gaming economy in which users may actually own a stake in the ecosystems they contribute to. This integration promises to improve the gaming experience by introducing new levels of player ownership and economic models, potentially changing how players interact with games. For instance, users of blockchain technology may own and exchange digital assets called Axies in the game Axie Infinity. This gives players a sense of control and ownership over the in-game items they possess, as well as a monetary value for the time and effort they invest in the game. Unveiling Robust Growth: Treasury Report Q1 2024 A compelling financial tale is presented in the treasury report for the first quarter of 2024 that is associated with the Merit Circle ecosystem. The information indicates that the overall assets of the ecosystem increased by more than $94 million, reaching a total of $201 million. This large gain is more than just a figure; it reflects the excellent strategic decision that Merit Circle made to join in the gaming sector, which is known for its rapid speed. Merit Circle As a very profitable enterprise, Saga stands out among significant investments as a noteworthy investment. In the beginning, Saga was valued at $125 million when it acquired Merit Circle; however, the price of the company has since increased to almost $5 billion after it has been completely diluted. This milestone not only demonstrates the possibility for strategic early-stage engagement in high-growth enterprises, but it also demonstrates the additional financial rewards that may be gained via investing and gaining attention on a new front as a result of its active participation in game production, notably with the online party game Forgotten Playland. The Treasury Department had around $30 million in $FP tokens at the end of the first quarter, which indicates that this game, which is scheduled to be published in April 2024, has already had a substantial influence on the financial situation. This game production initiative, which goes beyond basic investment to actively impact the gaming environment with cutting-edge goods, has resulted in a significant expansion of Merit Circle's operational reach, which has seen a significant increase in its operational reach. The strategy that is being taken is reflective of a more widespread trend in the gaming business, which is that strategic expenditures are increasingly propelling the company forward. The dedication of Merit Circle to openness, which can be seen in the regular publication of treasury reports, is the source of the high industry standard that the company has established. In this way, stakeholders are provided with a comprehensive perspective of the organization's financial status as well as its strategic goals, which in turn stimulates trust. Merit Circle Final Thoughts Merit Circle's strategic expansion and inventive push is something that I believe to be both fascinating and instructive. In addition to demonstrating that the Treasury is in excellent financial condition, the report for the first quarter of 2024 also demonstrates that the Treasury has a distinct strategic vision that has the potential to influence the direction that the entertainment industry will take in the years to come. It is the belief of Merit Circle that the seamless incorporation of blockchain technology into gaming has the potential to usher in a new era in which gaming is more than just a platform for economic involvement and progress; rather, it is also an experience that is pleasant. Don’t forget to like and share the story! Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR. Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program . HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR. Vested Interest Disclosure: business blogging program business blogging program

Merit Circle's Q1 2024 Treasury Report Reveals $94M Growth, Setting New Standards in GameFi

CHICAGO, United States, April 17th, 2024/Chainwire/-- Liquid Mercury, a leading crypto trading technology provider, announced today that it is providing a request for quote (RFQ) platform to GFO-X, the UK’s first regulated and centrally cleared trading venue dedicated to digital assets derivatives. Institutional traders will be able to source deep liquidity for trading listed options and futures contracts in large trade sizes that can be transacted as single-leg or complex, multi-leg strategies. Liquid Mercury’s institutional-grade platform, combined with GFO-X’s regulated derivatives marketplace, provides a secure and efficient trading environment for institutional trading firms and professional investors. This technology allows market participants to manage digital asset exposure and hedge underlying portfolio risks. Liquidity providers can provide two-sided quotes in response to RFQs generated by hedge funds, asset managers, and other buy-side participants initiated from Liquid Mercury’s professional-grade trading interface or FIX and web socket APIs. Trades will print on GFO-X’s exchange and be cleared through a GFO-X general clearing member at LCH SA, LCH Group’s continental Europe clearing house, which is part of the largest and most sophisticated clearing services in financial markets. Brokers will also use Liquid Mercury’s platform to intermediate trades between buyers and sellers in order to cross-block trades on GFO-X’s exchange and conduct other sell-side trading workflows. “Looking at the equity derivatives market, where the growth in number of options exchanges has fueled the growth in trading participants, added more liquidity in the market, and driven record volumes of contracts traded, I see GFO-X’s entrance into crypto derivatives as a key development for a more vibrant exchange landscape. This is a prime opportunity to grow the entire crypto options market,” said Liquid Mercury CEO, Tony Saliba. “In addition to providing the technology solution that will power GFO-X’s block liquidity network, Liquid Mercury will be operating as a strategic partner, introducing our clients and broad network of traditional market players as new participants to GFO-X’s exchange.”“We chose Liquid Mercury as a partner due to the team’s pedigree in building successful technology-focused franchises in traditional markets, combined with a track record of delivering institutional-grade solutions to the professional crypto trading community,” said GFO-X CEO, Arnab Sen. “Our shared vision of a regulated derivatives market that is purpose-built for digital assets using the best market standards and technology from traditional finance underpins what we expect to be a long and very fruitful strategic partnership.” For more information about Liquid Mercury’s suite of crypto trading technology solutions, users can visit www.liquidmercury.com. About Liquid Mercury Liquid Mercury powers professional crypto trading. Liquid Mercury is the #1 choice for sophisticated buy-side and institutional sell-side legacy trading professionals moving into crypto. Institutional grade infrastructure, access to deep liquidity, and best-in-class trading tools and workflow automation; Liquid Mercury was built by professionals for professionals. For more information about Liquid Mercury, visit www.liquidmercury.com. About GFO-X Global Futures and Options Ltd (GFO-X) is the UK's first regulated and centrally cleared trading venue dedicated to digital asset derivatives. UK FCA authorized and regulated GFO-X, which is connected to the world's largest financial institutions, and has partnered with leading clearing house LCH SA, solving counterparty and credit risk. GFO-X services the significant unmet need of large global institutional participants who require a highly regulated, institutional-grade trading venue for digital asset derivatives. GFO-X’s mission is to deliver enhanced liquidity, institutional connectivity, and risk mitigation through high-performance technology and optimized contract specifications. Further information can be found at www.gfo-x.com Contact Chief Commercial Officer Ryan Hansen Liquid Mercury team@liquidmercury.com This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here. CHICAGO, United States, April 17th, 2024/Chainwire/-- Liquid Mercury, a leading crypto trading technology provider, announced today that it is providing a request for quote (RFQ) platform to GFO-X , the UK’s first regulated and centrally cleared trading venue dedicated to digital assets derivatives. Institutional traders will be able to source deep liquidity for trading listed options and futures contracts in large trade sizes that can be transacted as single-leg or complex, multi-leg strategies. GFO-X GFO-X Liquid Mercury’s institutional-grade platform, combined with GFO-X’s regulated derivatives marketplace, provides a secure and efficient trading environment for institutional trading firms and professional investors. This technology allows market participants to manage digital asset exposure and hedge underlying portfolio risks. Liquidity providers can provide two-sided quotes in response to RFQs generated by hedge funds, asset managers, and other buy-side participants initiated from Liquid Mercury’s professional-grade trading interface or FIX and web socket APIs. Trades will print on GFO-X’s exchange and be cleared through a GFO-X general clearing member at LCH SA, LCH Group’s continental Europe clearing house, which is part of the largest and most sophisticated clearing services in financial markets. Brokers will also use Liquid Mercury’s platform to intermediate trades between buyers and sellers in order to cross-block trades on GFO-X’s exchange and conduct other sell-side trading workflows. “Looking at the equity derivatives market, where the growth in number of options exchanges has fueled the growth in trading participants, added more liquidity in the market, and driven record volumes of contracts traded, I see GFO-X’s entrance into crypto derivatives as a key development for a more vibrant exchange landscape. This is a prime opportunity to grow the entire crypto options market,” said Liquid Mercury CEO, Tony Saliba. “Looking at the equity derivatives market, where the growth in number of options exchanges has fueled the growth in trading participants, added more liquidity in the market, and driven record volumes of contracts traded, I see GFO-X’s entrance into crypto derivatives as a key development for a more vibrant exchange landscape. This is a prime opportunity to grow the entire crypto options market,” said Liquid Mercury CEO, Tony Saliba. “In addition to providing the technology solution that will power GFO-X’s block liquidity network, Liquid Mercury will be operating as a strategic partner, introducing our clients and broad network of traditional market players as new participants to GFO-X’s exchange.”“We chose Liquid Mercury as a partner due to the team’s pedigree in building successful technology-focused franchises in traditional markets, combined with a track record of delivering institutional-grade solutions to the professional crypto trading community,” said GFO-X CEO, Arnab Sen. “In addition to providing the technology solution that will power GFO-X’s block liquidity network, Liquid Mercury will be operating as a strategic partner, introducing our clients and broad network of traditional market players as new participants to GFO-X’s exchange.”“We chose Liquid Mercury as a partner due to the team’s pedigree in building successful technology-focused franchises in traditional markets, combined with a track record of delivering institutional-grade solutions to the professional crypto trading community,” said GFO-X CEO, Arnab Sen. “Our shared vision of a regulated derivatives market that is purpose-built for digital assets using the best market standards and technology from traditional finance underpins what we expect to be a long and very fruitful strategic partnership.” “Our shared vision of a regulated derivatives market that is purpose-built for digital assets using the best market standards and technology from traditional finance underpins what we expect to be a long and very fruitful strategic partnership.” For more information about Liquid Mercury’s suite of crypto trading technology solutions, users can visit www.liquidmercury.com . www.liquidmercury.com www.liquidmercury.com About Liquid Mercury Liquid Mercury powers professional crypto trading. Liquid Mercury is the #1 choice for sophisticated buy-side and institutional sell-side legacy trading professionals moving into crypto. Institutional grade infrastructure, access to deep liquidity, and best-in-class trading tools and workflow automation; Liquid Mercury was built by professionals for professionals. For more information about Liquid Mercury, visit www.liquidmercury.com . Liquid Mercury Liquid Mercury www.liquidmercury.com www.liquidmercury.com About GFO-X Global Futures and Options Ltd (GFO-X) is the UK's first regulated and centrally cleared trading venue dedicated to digital asset derivatives. UK FCA authorized and regulated GFO-X, which is connected to the world's largest financial institutions, and has partnered with leading clearing house LCH SA, solving counterparty and credit risk. GFO-X services the significant unmet need of large global institutional participants who require a highly regulated, institutional-grade trading venue for digital asset derivatives. Global Futures and Options Global Futures and Options GFO-X’s mission is to deliver enhanced liquidity, institutional connectivity, and risk mitigation through high-performance technology and optimized contract specifications. Further information can be found at www.gfo-x.com www.gfo-x.com www.gfo-x.com Contact Chief Commercial Officer Ryan Hansen Liquid Mercury team@liquidmercury.com This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here. This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here. here. here.

Liquid Mercury Partners With GFO-X To Provide RFQ Platform For Trading Crypto Derivatives

Authors: (1) Muzhaffar Hazman, University of Galway, Ireland; (2) Susan McKeever, Technological University Dublin, Ireland; (3) Josephine Griffith, University of Galway, Ireland. Table of Links Abstract and Introduction Related Works Methodology Results Limitations and Future Works Conclusion, Acknowledgments, and References A Hyperparameters and Settings B Metric: Weighted F1-Score C Architectural Details D Performance Benchmarking E Contingency Table: Baseline vs. Text-STILT B Metric: Weighted F1-Score The performance of our models are measured by Weighted F1-Score, inline with the reporting set by the authors of the Memotion 2.0 dataset (Patwa et al., 2022). The F1-Score is the harmonic mean of precision and recall, equally representing both. “Weighted” here denotes that the F1-score is first computed per-class and then averaged while weighted by the proportion of occurrences of each class in the ground truth labels. We compute this using PyTorch’s implementation multiclass f1 score. Class-wise F1-scores, F1c where c ∈ [1, 2, 3], are computed as: Where Nc is the number of samples with the ground truth label c in the testing set. The Weighted F1 is often used when the classes are imbalanced – the training, validation and testing sets of Memotion 2.0 show significant and varying class imblance – as it takes into account the relative importance of each class. Note that this weighted averaging could result in an F1-score that is not between the Precision and Recall scores. This paper is under CC 4.0 license. available on arxiv

Unimodal Training for Multimodal Meme Sentiment Classification—Metric: Weighted F1-Score

This paper is under CC 4.0 license. available on arxiv Authors: (1) Joel Jang, CarperAI,University of Washington & Allen Institute for AI; (2) Seungone Kim, KAIST AI; (3) Yizhong Wang, University of Washington; (4) Jack Hessel, University of Washington; (5) Luke Zettlemoyer, Aleph Alpha; (6) Hannaneh Hajishirzi, University of Washington & Allen Institute for AI; (7) Yejin Choi, UC San Diego. 5 CONCLUSION Previous work has shown that adapting LLMs with RLHF helps them generate outputs that are preferred by humans over the supervised fine-tuned counterpart. However, recent work has also pointed out that simply training LLMs to abide by the preference of the general may result in ignoring individual preferences and values. In this work, we provide the first steps to tackle this issue by proposing Reinforcement Learning from Personalized Human Feedback as a multi-objective problem so that LLMs can be aligned to follow conflicting preferences. We propose a promising method called P-SOUPS that is able to composite models trained on single objectives on the fly during inference. We also highlight the scalability of P-SOUPS by showing that it scales linearly, instead of exponentially like the MORL baseline, with regards to the number of new preferences, which is required to provide true personalization to individual users. ACKNOWLEDGMENTS Thanks to Minyoung Hwang, Sungdong Kim, Tim Dettmers, Yoonjoo Lee, and Margaret Li for helpful feedback and discussion. REFERENCES Xiang Ao, Xiting Wang, Ling Luo, Ying Qiao, Qing He, and Xing Xie. PENS: A dataset and generic framework for personalized news headline generation. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 82–92, Online, August 2021. Association for Computational Linguistics. doi: 10.18653/v1/2021.acl-long.7. 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We also provide 3 demonstrations: one scenario where there is a tie because both responses do not contain any notion of the preference (e.g. both responses do not show any signs of friendliness), one scenario where there is a clear winner, and one scenario where they are both good, but one is better than the other. Simulated pairwise evaluation We recruited 24 crowd workers for our human evaluation. Figure 5 shows the interface used for human evaluation. We consider both the ‘Tie’ and ‘Both are bad’ options to be Ties. B CRITERIA-WISE EVALUATION The criteria-wise win rate (%) across all the methods are shown in Figure 6. The criteria-wise win rate was calculated by getting the average win-rate of the preference combinations that contained the specific preference dimension. For example, when calculating the criteria-wise win rate of ‘Elementary’, we got the average win-rate of the preference combinations that contained the ‘Elementary’ preference, which includes AAA, AAB, ABA, and ABB. C THE FULL LIST OF EVALUATION PROMPTS The full list of evaluation prompts used in our experiments are provided in Table 5 D DETAILED RESULTS FOR HUMAN EVALUATION AND GPT-4 EVALUATION We provide detailed results (win / loss / tie for each of the preference combinations for our main experimental results. Table 6 shows the GPT4 evaluation and Table 7 shows the human evaluation results. E EXAMPLES OF P-SOUPS TEXT GENERATIONS Table 8 shows empirical examples of the text generated from each preference combination of the 16 preference combination experiments for the same prompt.

Personalized Soups: LLM Alignment Via Parameter Merging - Conclusion & References

This paper is under CC 4.0 license. available on arxiv Authors: (1) Joel Jang, CarperAI,University of Washington & Allen Institute for AI; (2) Seungone Kim, KAIST AI; (3) Yizhong Wang, University of Washington; (4) Jack Hessel, University of Washington; (5) Luke Zettlemoyer, Aleph Alpha; (6) Hannaneh Hajishirzi, University of Washington & Allen Institute for AI; (7) Yejin Choi, UC San Diego. 4 EXPERIMENTS 4.1 BASELINE METHODS In this subsection, we provide details of the single-objective baseline methods we implement. The summary of the key component differences in comparison with our proposed methods is provided in Table 2. As the most simple baseline, we simply utilize the base Tulu-7B model to generate responses without providing it any notion of preferences. During the evaluation, we use the same response to evaluate on the 8 different preference combinations. Vanilla Baseline (VB) We perform RLHF in the traditional manner where GPT-4 labels which response is generally better, train a reward model using the pairwise feedback data, and use the reward model to adapt the policy model with PPO training. The same 10k instances from Dtrain are used for RLHF. Reinforcement Learning from Human Feedback (RLHF) Next, we observe how far the instruction-tuned base LM can integrate multiple preference combinations by simply prompting for the preferences without any additional training Preference Prompting (PP) For a competitive baseline, we utilize the positive candidate selected by GPT-4 as the output for imitation learning, which is essentially performing rejection sampling (Nakano et al., 2021a) that uses GPT-4 as the reward model for selecting golden responses from the distribution of responses. We append the individual preference prompt with instances from Dtrain and multitask train the Tulu-7B model across all six individual preferences. This method also allows distilling the outputs of Tulu-30B for training Tulu-7B. Multi-task Training (MT) 4.2 EXPERIMENTAL DETAILS For both the reward model and policy training, we limit ourselves to going through Dtrain only once (1 epoch). In the initial exploration stage, the end performance for the policy model did not improve even if we trained the reward model for longer epochs. For policy model training, we utilize our evaluation dataset (50 prompts) to get the average reward and chose the policy model checkpoint that showed the highest average reward on the evaluation set for our final evaluation. We utilize LoRA (Hu et al., 2022) for both the reward model and policy model training. The detailed hyperparameters for the reward model and policy model training are provided in our github repository [5] . 4.3 MAIN RESULTS Table 3 and 4 show the results of doing all possible pairwise comparisons across the methods using GPT-4 and humans as judges, respectively. Note that the win rate of each battle is calculated using the aggregated win rate explained in Section 3.3. Each individual preference combination results are shown in Appendix D. We also show the average criteria-wise win rate instead of the aggregated win rate across all of the methods in Appendix B. The first thing to note is that there is a limitation to the extent prompting (PP) can integrate multiple preferences. This means that specific training for integrating the multiple preferences is necessary to composite them together. Next, we can see that supervised fine-tuning (RS) underperforms MORLbased methods, which is consistent with prior work that also showed the advantage of RL-based approaches when aligning LLMs with human feedback compared to its supervised-finetuning counterpart. Finally, while P-MORL and P-SOUPS both outperform other methods on average, there exists a discrepancy between the simulated and human evaluation; P-SOUPS has the highest average win rate in GPT-4 evaluation while P-MORL has the highest in human evaluation. Nonetheless,P-SOUPS is able to show superior performance in comparison to baseline methods and competitive performance to P-MORL. In previous parameter merging literature, multi-task fine-tuning (RS) used to be considered the upper bound for compositional abilities through parameter merging (Don-Yehiya et al., 2022). However, in our scenario, we can see that parameter merging (P-SOUPS) is able to outperform multitask fine-tuning, showing promising results for parameter merging not only as a distributed multitask finetuning method but a method that can result in superior performance than multitask training. One might still wonder about the general ‘helpfulness’ capabilities of models that are trained to be tailored to multiple preferences. In Figure 3, we first show the average pairwise win rate from Table 3 in green. Next, we instead ONLY perform pairwise comparisons with the unseen objective ‘helpfulness’ (ask GPT-4 which model response they generally prefer better) and report the average pairwise win rate in red. Trade-off Between General Helpfulness RLHF performs the best in this scenario, which shows that there is no free lunch; while the objective of RLHF was to provide model responses that are generally preferred (highly correlated with ‘helpfulness’), the other methods were prompted/trained to be optimized towards the personalized preference aspects, possibly deviating away from general helpfulness. While RS, P-MORL, and P-SOUPS are able to retain similar performance in terms of helpfulness compared to the initial instructiontuned model (VB), we observe that prompting (PP) significantly underperforms compared to other methods which also highlights the limitation of simply prompting base/instruction-tuned models for personalized preferences and shows the need for specialized training methods for personalization. 4.4 SCALING TO NEW PREFERENCES While we explore 6 distinct preferences in this work, we are still limited in doing ‘declarative’ personalization; that is, the individual preferences have been pre-defined to measure the performance of different methodologies. However, in the real world, individuals may not be bound by predefined preferences. Furthermore, people’s preferences might change over time, which requires continual learning of new preferences. This means that we may be required to train infinite numbers of preferences to be truly personalized to individuals’ preferences. Considering this aspect, the scalability of methods becomes a critical factor in implementing RLPHF in real-world scenarios. In order to compare the scalability of P-MORL and P-SOUPS, we add two new preferences (in addition to the ones in Table 1 to the STYLE dimensions: (P3C) “Generate/Choose a response (that answers) in a sassy manner.” and (P3D) “Generate/Choose a response (that answers) in a sarcastic manner.”, which results in a total of 16 (2 × 2 × 4) unique preference combinations. We re-train P-MORL on the 16 new preference combinations and only train two new policy models for integrating P-SOUPS. The simulated win-rate between P-MORL and P-SOUPS on each of the original preference combinations (53.04% win rate of P-SOUP over P-MORL in Table 3 decomposed into each preference combinations) and the 16 new preference combinations are shown in Figure 4. As shown in the figure, P-SOUPS shows competitive performance compared to P-MORL while being much more efficient considering that it (1) did not have to observe all 16 possible preference combinations and (2) did not have to re-train on the previous preferences, but just train two new policies each for the new preference in a modular manner and merge their parameters on-the-fly during inference. Considering that P-MORL is bounded by O(2n) while P-SOUPS is bounded by O(n) where n is the total number of preferences (assuming there are two unique preferences for each dimension), we assert that P-SOUPS allows tackling RLPHF to be feasible. [5] https://github.com/joeljang/RLPHF

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