Papers with Critical Step Optimization

    1 papers
    Verified Critical Step Optimization for LLM Agents (2026.findings-acl)

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    Challenge: Critical Step Optimization (CSO) focuses preference learning on verified critical steps where alternative actions demonstrably flip task outcomes from failure to success.
    Approach: They propose a method which focuses preference learning on verified critical steps where alternative actions demonstrably flip task outcomes from failure to success.
    Outcome: The proposed method outperforms the existing methods on GAIA-Text-103 and XBench-DeepSearch while requiring supervision at only 16% of trajectory steps.

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