AI RESEARCH
Where to Spend Rollouts: Hit-Utility Optimal Rollout Allocation for Group-Based RLVR
arXiv CS.LG
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ArXi:2605.07114v1 Announce Type: new Reinforcement learning with verifiable rewards (RLVR) has emerged as a central paradigm for improving the reasoning capabilities of large language models. Group-based policy optimization methods, such as GRPO, typically allocate a fixed number of rollouts to every prompt. This uniform allocation can be inefficient: it over-allocates compute to prompts whose sampled groups are already saturated while under-exploring prompts for which additional samples may reveal useful correct trajectories. To address this limitation, we.