Recoleta Item Note
SWE-Fuse: Empowering Software Agents via Issue-free Trajectory Learning and Entropy-aware RLVR Training
SWE-Fuse is a training framework for software repair agents whose core goal is to reduce the misleading effects of low-quality issue descriptions in real-world software problems. By combining trajectory data with issue…
Summary
SWE-Fuse is a training framework for software repair agents whose core goal is to reduce the misleading effects of low-quality issue descriptions in real-world software problems. By combining trajectory data with issue descriptions and without issue descriptions, together with entropy-aware RLVR training, it improves solve rates on SWE-bench Verified.
Problem
- The paper addresses the fact that in real-world software repair data, issue descriptions often do not match the actual patches, which can lead automated software agents astray and cause debugging and patch generation to fail.
- This matters because current SWE agents rely heavily on issue text as the task entry point; once the description is noisy, missing, or misleading, the agent may search in the wrong direction even if it has strong coding ability.
- Data scale and quality are also limited. For example, the paper notes that in SWE-smith, 18,033 / 59,136 (30.49%) of samples have empty problem descriptions, indicating that relying only on high-quality issue supervision is difficult to scale.
Approach
- The core idea is simple: do not only teach the model to read an issue and fix a bug, but also teach it to find problems through tests and debugging on its own even without a reliable issue description.
- To do this, the authors build a hybrid training framework that fuses two types of samples: one with issue descriptions, and another consisting of issue-free samples that retain only tests and the environment, allowing the model to learn problem localization through multi-round debugging.
- In the supervised learning stage, the authors first use a teacher agent to generate multi-step ReAct trajectories (explicitly including reasoning and bash action), then filter them by removing trajectories with poor formatting, no intermediate reasoning, or possible cheating via git metadata, ultimately obtaining 14k high-quality trajectory data.
- In the reinforcement learning stage, the authors propose entropy-aware RLVR: if the model currently has high uncertainty and the sample advantage is positive, clipping is relaxed to encourage more exploration; if the advantage is non-positive and uncertainty is high, training is made more conservative to avoid over-penalizing potentially useful exploration because of noise.
- The training and execution environment is kept relatively simple, relying mainly on basic bash tool calls and sandbox execution rather than a more complex specialized toolchain.
Results
- On SWE-bench Verified, the authors report that SWE-Fuse-Qwen3-8B achieves a solve rate of 43.0%, and SWE-Fuse-Qwen3-32B reaches 60.2%.
- Compared with the best baselines, the paper claims that SWE-Fuse achieves solve rates of 43.0% and 60.2% for the 8B and 32B settings, respectively, and further describes these in the main text as relative improvements of 9.1% (8B) and 11.7% (32B).
- After adding test-time scaling TTS@8, the solve rates of the 8B and 32B models further increase to 49.8% and 65.2%.
- The paper states that the 32B open-source model reaches the best performance among same-size open-source models at the time, and has a resolved rate 1.8% higher than OpenAI-o3, though it still trails Claude-4-Sonnet and Claude-4.5-Sonnet.
- The authors also release a trajectory dataset with 14,350 valid trajectories, covering 14,329 instances and 111 projects; the total number of interaction rounds is 401,958, with an average of 28.05 rounds and average token consumption of 19,676.08.
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