Where Hindsight Credit Can Reside: A Signed-Capacity View of Token Updates in RLVR

arXiv:2604.11056v2 Announce Type: replace Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) improves the reasoning ability of Large Language Models (LLMs), but sparse outcome rewards make token-level credit assignment difficult. We study token-level credit as a reward-conditioned shift from the behavior policy to a hindsight posterior. In autoregressive RLVR, this shift can be expressed through Conditional Mutual Information (CMI), which shows that token entropy upper-bounds possible hindsight credit. Entropy, however, indicates capacity rather than update direction, so we introd

Source: arXiv cs.LG — read the full report at the original publisher.

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