arXiv cs.AI· Xudong Cai, Zhengxi Lu, Yuchen Yan, Jian Shao, Jun Xiao, Yueting Zhuang, Yongliang Shen·· 2 小时前AI 评分37
SWE-Ladder：通过渐进式特权自蒸馏学习定位、复现与修补
SWE-Ladder: Learning to Localize, Reproduce, and Patch via Progressive Privileged Self-Distillation
AI 导读
新方法 SWE-Ladder 将每个训练实例的已验证修复拆解为阶段化证据——变更文件与行用于定位、fail-to-pass 测试用于复现、diff 用于修补，由规则路由器按工具调用分配阶段，冻结教师模型仅见对应证据，并按修复顺序蒸馏，同时用 GRPO 优化完整轨迹。
正文 · 原文
Abstract:Resolving a repository-level issue typically requires a software-engineering (SWE) agent to localize the faulty code, reproduce the failure, and patch the code. Reinforcement learning trains such agents from a single test result on the final patch, while on-policy self-distillation (OPSD) adds token-level supervision from a teacher with privileged evidence, exposing the full reference patch at every step. Neither provides stage-specific guidance for localization or reproduction. We propose SWE-Ladder, which turns the verified repair of each training instance into stage-specific evidence: the changed files and lines for localization, the fail-to-pass tests for reproduction, and the diff for patching. A rule-based router assigns each step of an end-to-end rollout to a stage based on its tool call, a frozen teacher sees only the matching evidence, and the stages are distilled in repair order while GRPO optimizes the full trajectory. With Qwen3-4B-Instruct-2507 on SWE-bench Verified under the R2E-Gym scaffold, SWE-Ladder improves pass@1 from 9.6% to 16.4%, compared with 10.4% for GRPO and 12.4% for OPSD with the full patch. Under the OpenHands scaffold, it improves pass@1 from 13.0% to 17.6%. Reversing the stage order under R2E-Gym reduces pass@1 to 11.4%.
| Comments: | 18 pages, 11 figures. Code: this https URL |
| Subjects: | Artificial Intelligence (cs.AI); Software Engineering (cs.SE) |
| Cite as: | arXiv:2610.33667 [cs.AI] |
| (or arXiv:2610.33667v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.33667 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Xudong Cai [view email]
[v1]
Sat, 24 Oct 2026 13:27:18 UTC (3,767 KB)
来源：arXiv cs.AI · arxiv.org