Nghi Dao, Calder Morton-Ferguson, Noah Golowich, Adam Block · 2026 · arXiv
DOI: 10.48550/arXiv.2610.28770 · open-access PDF
RLVR improves language-model reasoning, but commonly relies on independent stochastic rollouts for exploration, which can repeatedly sample similar failed strategies. Prior work has seen success with approaches that guide exploration by introducing dependence into the sampling process by including incorrect solutions in the context for subsequent attempts. Still, these methods introduce the additional question of how to assign credit to each attempt. We propose to view the credit assignment through the lens of Stochastic Shortest Paths, a framework in classical reinforcement learning that seek
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