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Success Leaves Detours: Learning Executable Walkthroughs for Long-Horizon Agents

arXiv:2609.22120v1 Announce Type: new Abstract: Test-time self-evolving agents improve by reusing past experience, yet sparse-reward trajectories contain failures, loops, and detours, while summaries often omit the state conditions and action dependencies needed for execution. We study executable W…

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  1. 2026-09-22 04:00 · arXiv cs.LG
    Success Leaves Detours: Learning Executable Walkthroughs for Long-Horizon Agents

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