Actor-Curator: Scalable Adaptive Curriculum Learning for LLM Post-Training

Zhengyao Gu, Jonathan Light, Raul Astudillo, Ziyu Ye, Langzhou He, Wei Cheng, Santiago Paternain, Philip S. Yu, Yisong Yue

Keywords: large language models, RL post-training, curriculum learning, adaptive data selection, policy optimization

TL;DR: A learned curator adaptively selects training problems to maximize policy improvement during RL post-training of LLMs

Abstract: 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.

Submission Number: 88