arXiv: cs.AI preprintsInternational5 November 2026
GIST: Gibbs-Targeted Hierarchical Data Selection for Offline RLVR
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arXiv:2611.06395v1 Announce Type: new Abstract: Offline reinforcement learning with verifiable rewards (RLVR) trains on a fixed pool of sampled responses and typically supervises every token of every response, although responses and tokens are not equally informative. We study which ones to supervise through the Gibbs-optimal policy of KL-regularized RLVR, which offline training targets, and measure the mismatch between a policy and this target with a bidirectional KL divergence. At the reference policy, this mismatch factorizes exactly and separates the two outcomes: correct responses contrib
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