Reinforcement Learning from Rich Feedback with Distributional DAgger 事件

Reinforcement Learning from Rich Feedback with Distributional DAgger arXiv:2606.05152v1 Announce Type: cross Abstract: Reasoning models have advanced rapidly, but the dominant reinforcement learning from verifiable rewards (RLVR) recipe remains surprisingly narrow: sample many responses and reward each with a single bit indicating whether the final answer is correct. Yet many settings provide rich feedback, including execution traces, tool outputs, expert corrections, and model self-evaluations