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

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. However, on difficult