ACTOR-CURATOR: Online Curriculum Learning via Policy Improvement Bandits for RL Post-Training
Zhengyao Gu · Jonathan Light · Raul Astudillo · Ziyu Ye · Langzhou He · Henry Peng Zou · Pei Chen · Wei Cheng · Santiago Paternain · Philip S. Yu · Yisong Yue
A learned curator adaptively selects training problems to maximize policy improvement during RL post-training of LLMs
摘要
Post-training large foundation models with reinforcement learning typically relies on massive and heterogeneous datasets, making effective curriculum learning both critical and challenging. In this work, we propose ACTOR-CURATOR, a scalable and fully automated curriculum learning framework for reinforcement learning post-training of large language models (LLMs). ACTOR-CURATOR learns a neural curator that dynamically selects training problems from large problem banks by directly optimizing for expected policy performance improvement. We formulate problem selection as a non-stationary stochastic bandit problem, derive a principled loss function based on online stochastic mirror descent, and establish regret guarantees under partial feedback. Empirically, ACTOR-CURATOR consistently outperforms uniform sampling and strong curriculum baselines across a wide range of challenging reasoning benchmarks, demonstrating improved training stability and efficiency. Notably, it achieves relative gains of 28.6% on AIME2024 and 30.5% on ARC-1D over the strongest baseline and up to 80% speedup. These results suggest that ACTOR-CURATOR is a powerful and practical approach for scalable LLM post-training.