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Learning from All-Failed Groups via First-Visit Coverage in Long-Horizon Agentic Tasks

Single source1 source12 Oct 2026

arXiv:2607.22724v2 Announce Type: replace-cross Abstract: Group-based policy optimization has been increasingly used to train large language model (LLM) agents from sparse outcome rewards by comparing trajectories or steps within a group.

Reported by 1 source

arxiv.orghttps://arxiv.org/abs/2607.22724

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