Reasoning critics enable better parallel search for software engineering agents

The text discusses the development and evaluation of different critic models to improve software engineering agents, focusing on regression-based and reasoning (chain-of-thought) critics. Regression-based critics, while beneficial, have limitations such as vulnerability to adversarial examples and limited capacity for adaptive reasoning. Reasoning critics, trained using reinforcement learning, aim to address these by evaluating agent trajectories with chain-of-thought reasoning, potentially making them more robust to out-of-distribution inputs. The study explores various training methods, including precision-prioritizing and balanced training, and finds that precision-prioritizing critics often perform better, particularly in scenarios where avoiding false positives is crucial. The text also highlights the potential of reasoning critics to generalize better to different environments and policies, though challenges remain in achieving oracle-level performance due to incomplete trajectory information. The authors propose further research into execution-based validation and process supervision using RL-trained models as promising avenues for enhancing critic performance.

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