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Contrastive Branch Policy Optimization

arXiv · AI, language, vision and robotics · article · Aug 25, 2026 · UTC

Reinforcement learning with verifiable rewards (RLVR) enables language models to learn multi-turn interaction with external tools, yet its sparse outcome rewards provide no signal for identifying which intermediate decisions are responsible for success. Branch sampling induces local comparisons among alternative continuations, but existing methods tend to conflate two distinct problems: allocating a fixed rollout budget and translating branch outcomes into token-level credit. We introduce Contrastive Branch Policy Optimization (CBPO), which disentangles these two problems and assigns a dedicat

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Evidence & attribution

arXiv · AI, language, vision and robotics · 2026-08-25T09:25:55.000Z

First collected: 2026-09-21T10:02:02.728Z. This is not the publication date.