AIBullisharXiv – CS AI · 9h ago7/10
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Reinforcement Learning from Rich Feedback with Distributional DAgger
Researchers introduce DistIL, a distributional variant of the DAgger imitation learning algorithm that leverages rich feedback signals beyond binary correctness labels to improve AI reasoning models. The approach uses forward cross-entropy objectives to enable better credit assignment and demonstrates monotonic policy improvement guarantees, outperforming standard reinforcement learning methods across scientific reasoning, coding, and mathematical problem-solving tasks.