AI RESEARCH
Contextual Rollout Bandits for Reinforcement Learning with Verifiable Rewards
arXiv CS.AI
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ArXi:2602.08499v2 Announce Type: replace-cross Reinforcement Learning with Verifiable Rewards (RLVR) is an effective paradigm for improving the reasoning capabilities of large language models. However, existing RLVR methods utilize rollouts in an indiscriminate and short-horizon manner: responses of heterogeneous quality within each prompt are treated uniformly, and historical rollouts are discarded after a single use. This leads to noisy supervision, poor sample efficiency, and suboptimal policy updates.