CAFE: Self-Improving Search Agents Need Co-Evolving Feedback
[Sztuczna inteligencja] Autorzy: Boyang Liu, Senjie Jin, Peixin Wang i 15 więcej
Outcome-supervised search agents learn when and how to retrieve evidence, but terminal rewards neither localize intermediate errors nor redirect an ongoing trajectory before those errors compound. Treating corrective feedback as a learned in-trajectory intervention couples the two roles: the agent must decide when to request and use feedback, while the critic must infer useful corrections from outcome-confounded rollouts whose failure patterns shift as the agent improves. We introduce CAFE (Coupled Agent--Feedback Evolution), a framework in which a shared-parameter model alternates between search-agent and critic roles. CAFE initializes feedback-conditioned recovery from trajectories built around the base agent's own failures, then couples online and offline optimization. During online RL, a comparative feedback estimate uses a prompt-level call--skip success gap to shape request return…
- Kategoria arxiv
- cs.AI
- Kategoria
- Sztuczna inteligencja