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PivotOPD: Learning to Recover from Pivotal Mistakes in Multi-Turn Agents

The paper introduces PivotOPD, an on-policy distillation framework that helps language agents avoid critical mistakes and recover from them during multi-turn interactions. It uses a teacher model to provide corrective actions after pivotal errors, improving task success rates in environments like ALFWorld and SWE-Bench Verified.

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PublishedSeptember 30, 2026Yinghui He, Yapei Chang, Khushi Bhardwaj
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WHY IT MAY MATTER

This approach improves the reliability of language agents in complex tasks by addressing critical errors early and enabling recovery, which is particularly useful in applications requiring high task success rates.

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