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Understanding Enrichment in Reinforcement Learning

Kim, Jinwoo et al.
arXiv (All) · Papers
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machine-learning
When rewards are sparse, reinforcement learning with verifiable rewards (RLVR) often uses hints or intermediate guidance to generate more successful rollouts. This enrichment biases policy-gradient updates unless corrected via importance weights, but existing methods omit correction or truncate impo
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