Table of Links Abstract and 1. Introduction Abstract and 1. Introduction Background & Related Work


Method
3.1 Sampling Small Mutations
3.2 Policy
3.3 Value Network & Search
3.4 Architecture


Experiments
4.1 Environments
4.2 Baselines
4.3 Ablations


Conclusion, Acknowledgments and Disclosure of Funding, and References Background & Related Work Background & Related Work Background & Related Work Method
3.1 Sampling Small Mutations
3.2 Policy
3.3 Value Network & Search
3.4 Architecture Method Method 3.1 Sampling Small Mutations 3.1 Sampling Small Mutations 3.2 Policy 3.2 Policy 3.3 Value Network & Search 3.3 Value Network & Search 3.4 Architecture 3.4 Architecture Experiments
4.1 Environments
4.2 Baselines
4.3 Ablations Experiments 4.1 Environments 4.1 Environments 4.2 Baselines 4.2 Baselines 4.3 Ablations 4.3 Ablations Conclusion, Acknowledgments and Disclosure of Funding, and References Conclusion, Acknowledgments and Disclosure of Funding, and References Conclusion, Acknowledgments and Disclosure of Funding, and References Appendix Appendix A. Mutation Algorithm A. Mutation Algorithm B. Context-Free Grammars B. Context-Free Grammars C. Sketch Simulation C. Sketch Simulation D. Complexity Filtering D. Complexity Filtering E. Tree Path Algorithm E. Tree Path Algorithm F. Implementation Details F. Implementation Details E Tree Path Algorithm Algorithm 1 shows the high-level pseudocode for how we find the first step of mutations to transform tree A into tree B. We linearly walk down both trees until we find a node that is different. If the target node is small, i.e., its σ(z) ≤ σsmall, then we can simply mutate the source to the target. If the target node is larger, we sample a random small expression with the correct production rule, and compute the path from this small expression to the target. This gives us the first step to convert the source node to the target node. Repeatedly using Algorithm 1 gives us the full path to convert one expression to another. We note that this path is not necessarily the optimal path, but a valid path that is less noisy than the path we would get by simply chasing the last random mutation. Authors:
(1) Shreyas Kapur, University of California, Berkeley (srkp@cs.berkeley.edu);
(2) Erik Jenner, University of California, Berkeley (jenner@cs.berkeley.edu);
(3) Stuart Russell, University of California, Berkeley (russell@cs.berkeley.edu). Authors: Authors (1) Shreyas Kapur, University of California, Berkeley (srkp@cs.berkeley.edu); (2) Erik Jenner, University of California, Berkeley (jenner@cs.berkeley.edu); (3) Stuart Russell, University of California, Berkeley (russell@cs.berkeley.edu). This paper is available on arxiv under CC BY-SA 4.0 DEED license. This paper is available on arxiv under CC BY-SA 4.0 DEED license. available on arxiv available on arxiv

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The Role of Mutation Path Algorithms in Tree-Diffusion Program Synthesis

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