Learning at the Right Pace: Adaptive Data Scheduling Improves LLM Reinforcement Learning
Read the original on arXiv Computation and Language →The paper introduces Adaptive Data Scheduling (ADS), a dual-level framework that improves reinforcement learning for large language models by tailoring data sampling to both semantic clusters and the policy’s current capability. ADS first reorganizes training samples into semantic clusters and adjusts the inter-cluster sampling distribution to focus on clusters the policy can currently handle. Then, within each cluster, ADS selects prompts near the policy’s capability boundary to provide informative relative advantages. Experiments on three LLMs and seven reasoning benchmarks show ADS boosts average accuracy by 5.5% over Group Relative Policy Optimization (GRPO) and consistently benefits various RL objective designs.
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