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arXiv AI By Zhizhao Liu, Zhiliang Tian, Xi Wang, Zhihua Wen, Yihang Xiong, Zhiquan Lai, Dongsheng Li

Efficient RLVR Scheduling via Graph-Structured Online Difficulty Estimation

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Efficient RLVR Scheduling via Graph-Structured Online Difficulty Estimation proposes a plug‑and‑play graph‑based online difficulty estimator for reinforcement learning with verifiable rewards (RLVR). The method constructs a difficulty‑aware sample graph using semantic and reasoning similarities, introduces latent difficulty states with a Potts prior, aggregates rollout outcomes with a state‑level Beta‑Binomial model, and updates these estimates online via a mean‑field variational algorithm. This framework can be integrated into sample‑selection and rollout‑allocation schedulers, enabling difficulty‑adaptive exploration without dedicated probing and achieving better performance across multiple base models, RL schedulers, and benchmarks.

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