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 4.3 Ablations To understand the impact of our design decisions, we performed ablation studies on the simplified Rainbow environment using a smaller transformer model. First, we examined the effect of removing the current image (no REPL) from the policy network’s input. As shown in Figure 5(a), this drastically hindered performance, confirming the importance of a REPL-like interface observed by Ellis et al. [11]. Next, we investigated the necessity of our reverse mutation path algorithm. While training on the last mutation step alone provides a valid path, it introduces noise by potentially targeting suboptimal intermediate states. Figure 5(a) demonstrates that utilizing the reverse mutation path significantly improves performance, particularly in finding solutions with fewer steps. However, both methods eventually reach similar performance levels, suggesting that a noisy path, while less efficient, can still lead to a solution. Finally, we explored whether the incremental noise process is crucial, given our tree edit path algorithm. Couldn’t we directly sample two random expressions, calculate the path, and train the network to imitate it? We varied the training data composition between pure forward diffusion (ρ = 0.0) and pure random initialization (ρ = 1.0) as shown in Figure 5(b). We found that a small proportion (ρ = 0.2) of pure random initializations combined with forward diffusion yielded the best results. This suggests that forward diffusion provides a richer training distribution around target points, while random initialization teaches the model to navigate the program space more broadly. The emphasis on fine-grained edits from forward diffusion proves beneficial for achieving exact pixel matches in our evaluations. 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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