arXiv Machine Learning July 2, 2026 By Zhanyi Sun, Shuran Song

From Prior to Pro: Efficient Skill Mastery via Distribution Contractive RL Finetuning

Read the original on arXiv Machine Learning →

arXiv:2603. 10263v2 Announce Type: replace-cross Abstract: We introduce Distribution Contractive Reinforcement Learning (DICE-RL), a framework that uses reinforcement learning (RL) as a "distribution contraction" operator to refine pretrained generative robot policies.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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Support-Constrained RL Enables Real-World Policy Improvement without Real-World Experience

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