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New ReTerm agent learns to recover from coding errors, boosting performance

By PulseAugur Editorial · [2 sources] ·

Researchers have developed ReTerm, a novel self-improving terminal agent designed to overcome errors during complex coding tasks. The system utilizes a recovery training approach, generating a dataset called ReTerm-10K and employing an online reinforcement learning algorithm, ReTerm-GRPO. This method allows the agent to learn from its own failed attempts, leading to improved task completion rates. The ReTerm-9B model, built on Qwen3.5-9B, demonstrated a significant improvement, achieving 34.8% Pass@1 on the Terminal Bench 2.0, a 14.6-point increase over the base model. AI

IMPACT Enhances the reliability of AI agents in complex coding and execution tasks, potentially improving automation in software development and other domains.

RANK_REASON The cluster describes a new research paper detailing a novel method and model for improving terminal agents' error recovery capabilities.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New ReTerm agent learns to recover from coding errors, boosting performance

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The cluster describes a new research paper detailing a novel method and model for improving terminal agents' error recovery capabilities.
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2 independent sources
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paper, model release
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28 days old
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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Wei Xie, Kaituo Feng, Shiming Fu, Yong Liu, Xiangyu Yue, Shuangjia Zheng ·

    ReTerm: Recovery Training for Self-Improving Terminal Agents

    arXiv:2610.18484v1 Announce Type: new 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 failur…

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    ReTerm: Recovery Training for Self-Improving Terminal Agents

    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-impro…

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