HuggingFace Daily Papers·· 1 天前
ReTerm：让终端 Agent 从自身失败中学会恢复的自改进训练方法
ReTerm: Recovery Training for Self-Improving Terminal Agents
AI 导读
研究者提出 ReTerm，一种通过从自身失败的终端任务中学习恢复来实现自改进的终端 Agent。团队构建数据管线产出 ReTerm-10K 数据集用于 SFT 冷启动，并提出 ReTerm-GRPO 在线 RL 算法，从 Agent 自身失败状态出发生成多次独立自我恢复尝试。
正文
Abstract:Terminal agents have shown strong abilities in complex coding tasks. However, they often struggle to recover from errors during long-running execution (e.g., command errors, faulty implementations), eventually leading to task failures. To this end, we present ReTerm, a self-improving terminal agent trained by learning how to recover from its own failed terminal tasks. To support its training, we construct a customized data pipeline and produce ReTerm-10K, a high-quality dataset that provides effective recovery demonstrations for SFT cold start. Furthermore, we propose ReTerm-GRPO, an online RL algorithm that forks from the agent's own failure states to generate multiple independent self-recovery attempts. By learning from whether these attempts successfully complete the task, the agent self-improves through its own recovery experience. Built on Qwen3.5-9B, our ReTerm-9B achieves 34.8% Pass@1 on Terminal-Bench 2.0, improving over the base model by 14.6 points. Further analysis shows that our method achieves consistently higher recovery rates across diverse error categories compared with standard GRPO training. All code, data and model are released at this https URL.
| Comments: | Project page: this https URL |
| Subjects: | Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Software Engineering (cs.SE) |
| Cite as: | arXiv:2610.18484 [cs.AI] |
| (or arXiv:2610.18484v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.18484 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Kaituo Feng [view email]
[v1]
Tue, 13 Oct 2026 14:44:24 UTC (2,091 KB)
来源：HuggingFace Daily Papers · arxiv.org