Learning at the Right Pace: Adaptive Data Scheduling Improves LLM Reinforcement Learning
Large Language Models (LLMs) achieve remarkable reasoning capabilities through reinforcement learning (RL) post-training. However, existing RL post-training commonly relies on uniform data sampling, which ignores the semantic structure of the training data and the evolving capability of the training policy. To address these limitations, we propose Adaptive Data Scheduling (ADS), a dual-level data scheduling framework that combines adaptive sampling across semantic clusters with policy-boundary sample selection. At the cluster level, ADS organizes samples according to semantic patterns and adapts the inter-cluster sampling distribution to prioritize clusters within the policy's current rea...
| Authors | Zicheng Xu, Ruixuan Zhang, Yu-Neng Chuang et al. |
|---|---|
| Primary category | cs.CL |
| Published | 21 June 2026 |
| Last updated at source | 14 October 2026 |
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