Title: Scalable Supervising Software Agents with Patch Reasoner
Primary area: other topics in machine learning (i.e., none of the above)
Keywords: LLM, Software Engineering, Agent

Abstract: While large language model agents have advanced software engineering tasks, the unscalable nature of existing test-based supervision is limiting the potential improvement of data scaling. The reason is twofold: (1) building and running test sandbox is rather heavy and fragile, and (2) data with high-coverage tests is naturally rare and threatened by test hacking via edge cases. In this paper, we propose R4P, a patch verifier model to provide scalable rewards for training and testing SWE agents via reasoning. We consider that patch verification is fundamentally a reasoning task, mirroring how human repository maintainers review patches without writing and running new reproduction tests. To obtain sufficient reference and reduce the risk of reward hacking, R4P uses a group-wise objective for RL training, enabling it to verify multiple patches against each other's modification and gain a dense reward for stable training. R4P achieves 72.2\% Acc. for verifying patches from SWE-bench-verified, surpassing OpenAI o3. To demonstrate R4P's practicality, we design and train a lite scaffold, Mini-SE, with pure reinforcement learning where all rewards are derived from R4P. As a result, Mini-SE achieves 26.2\% Pass@1 on SWE-bench-verified, showing a 10.0\% improvement over the original Qwen3-32B.
This can be further improved to 33.8\% with R4P for test-time scaling. The stable scaling curves in both RL test rewards and test-time accuracy reflect R4P's practical utility for scalable supervision on software agents.

TL;DR: A reasoning-based patch verifier for test-free scalable supervision on software agents.