Source note
SWE-AGILE: A Software Agent Framework for Efficiently Managing Dynamic Reasoning Context
Summary
SWE-AGILE is a software agent framework for multi-step code fixing that keeps recent detailed reasoning, compresses older reasoning into short digests, and trains the model to work under that changing context. On SWE-Bench-Verified, it reports a new best result for 7B-8B open models, reaching 24.1% success with Qwen3-8B.
Problem
- Multi-turn software engineering agents need long reasoning chains to inspect code, use tools, and handle edge cases, but keeping all past reasoning makes the context grow fast.
- Large contexts hurt agent quality through lost-in-the-middle effects, higher memory cost, and slower training and inference.
- If old reasoning is dropped completely, the agent has to re-derive the same analysis after each tool call, which wastes tokens and weakens continuity across steps.
Approach
- The agent outputs three parts at each step: detailed reasoning, a short reasoning digest, and the next action.
- It uses a dynamic context: full observations and actions stay in history, recent reasoning stays verbatim in a sliding window, and older reasoning is replaced by per-step digests.
- Training uses trajectory snapshots so each step is learned under the same visibility limits used at inference time, instead of exposing the full past chain of thought during training.
- A hindsight backfill pipeline rewrites successful trajectories with detailed reasoning and digests, using the ground-truth future action plus the original shallow thought to synthesize better supervision.
- RL with verifiable rewards adds a trajectory-level compression reward, so the model gets rewarded only when it both solves the task and keeps the retained context short.
Results
- On SWE-Bench-Verified, SWE-AGILE (SFT+RL) with Qwen3-8B reaches 24.1% success.
- On the same benchmark, the Qwen3-8B base model gets 15.83%, and SWE-AGILE (SFT) gets 21.45%, a 35.5% relative improvement over the base model.
- The paper says 24.1% sets a new standard for the 7B-8B class in its comparison table, beating R2EGym 7B (19.0%), SWE-smith 7B (15.2%), and SWE-Gym 7B (10.6%); it also exceeds SkyRL-Agent-v0 14B (21.6%).
- The method uses 2.2k training trajectories, which the authors describe as 11% of the 19.3k trajectories used by SWE-Dev, while SWE-AGILE still slightly beats SWE-Dev 7B (23.4%) with its 24.1% score.
- With Qwen3-14B and SFT only, SWE-AGILE reports 30.06% on SWE-Bench-Verified, above R2EGym 14B (26.8%), SkyRL-Agent-v0 14B (21.6%), and SWE-Gym 14B (16.4%).
- On SWE-Bench Lite, SWE-AGILE-8B reports 14.77%, compared with SWE-smith-7B at 11.7% and R2E-Gym at 11.0%. The excerpt includes only partial ablation results for dynamic context analysis, so the full quantitative breakdown for those studies is not available here.