Planner-Centric Reinforcement Learning for Deep Research with Structure-Aware Reward 事件

Planner-Centric Reinforcement Learning for Deep Research with Structure-Aware Reward arXiv:2605.30824v1 Announce Type: new Abstract: Deep research tasks require LLMs to plan what to investigate, retrieve evidence, and synthesize long-form answers across multiple branches of inquiry. Existing training paradigms either rely on short-form verifiable QA as a proxy or optimize monolithic long trajectories, which makes planning and execution difficult to disentangle and yields weak credit assignment