Reinforcement Learning from Rich Feedback with Distributional DAgger
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. We study how to use such feedback through a distributional variant of the classic imitation learning algorithm DAgger, where the learner has local access to an expert distribution on states visited by the current policy. This yields a simple forward cross-entropy objective that admits a blackbox expert and whose s...
| Authors | Rishabh Agrawal, Jacob Fein-Ashley, Paria Rashidinejad |
|---|---|
| Primary category | cs.LG |
| Published | 3 June 2026 |
| Last updated at source | 5 June 2026 |
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