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Objective Mismatch in Reinforcement Learning from Human Feedback: Acknowledgments, and Referencesby@feedbackloop
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Objective Mismatch in Reinforcement Learning from Human Feedback: Acknowledgments, and References

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Discover the challenges of objective mismatch in RLHF for large language models, affecting the alignment between reward models and downstream performance. This paper explores the origins, manifestations, and potential solutions to address this issue, connecting insights from NLP and RL literature. Gain insights into fostering better RLHF practices for more effective and user-aligned language models.
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Authors:

(1) Nathan Lambert, Allen Institute for AI;

(2) Roberto Calandra, TU Dresden.

Abstract & Introduction

Related Work

Background

Understanding Objective Mismatch

Discussions

Conclusion

Acknowledgments, and References

Acknowledgments

This work was partly supported by the German Research Foundation (DFG, Deutsche Forschungsgemeinschaft) as part of Germany’s Excellence Strategy – EXC 2050/1 – Project ID 390696704 – Cluster of Excellence “Centre for Tactile Internet with Human-in-the-Loop” (CeTI) of Technische Universität Dresden, and by Bundesministerium für Bildung und Forschung (BMBF) and German Academic Exchange Service (DAAD) in project 57616814 (SECAI, School of Embedded and Composite AI).

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This paper is available on arxiv under CC 4.0 license.