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Rollback-Induced Reflection (RIR) is proposed, a unified recovery framework that restores execution to a selected prior state while carrying forward reusable knowledge distilled from the abandoned trajectory to guide subsequent decisions.
Task learnability can significantly enhance RL efficiency in LLMs, leading to better performance with less data.
BOUND achieves up to 5.6 EM points improvement over traditional methods by correcting local search-control errors in real-time.
