Understanding Enrichment in Reinforcement 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 importance weights in or...
Jinwoo Kim, Shraddha Barke
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