DUMP: Automated Distribution-Level Curriculum Learning for RL-based LLM
  Post-training
Authors: name: Zhenting Wang, name: Guofeng Cui, name: Kun Wan, name: Wentian Zhao

Recent advances in reinforcement learning (RL)-based post-training have led
to notable improvements in large language models (LLMs), particularly in
enhancing their reasoning capabilities to handle complex tasks. However, most
existing methods treat the training data as a unified whole, overlooking the
fact that modern LLM training often involves a mixture of data from diverse
distributions-varying in both source and difficulty. This heterogeneity
introduces a key challenge: how to adaptively schedule training across
distributions to optimize learning efficiency. In this paper, we present a
principled curriculum learning framework grounded in the notion of
distribution-level learnability. Our core insight is that the magnitude of
policy advantages reflects how much a model can still benefit from further
training on a given distribution. Based on this, we propose a
distribution-level curriculum learning framework for RL-based LLM
post-training, which leverages the Upper Confidence Bound (UCB) principle to
dynamically adjust sampling probabilities for different distrubutions. This
approach prioritizes distributions with either high average advantage
(exploitation) or low sample count (exploration), yielding an adaptive and
theoretically grounded training schedule. We instantiate our curriculum
learning framework with GRPO as the underlying RL algorithm and demonstrate its
effectiveness on logic reasoning datasets with multiple difficulties and
sources. Our experiments show that our framework significantly improves
convergence speed and final performance, highlighting the value of
distribution-aware curriculum strategies in LLM post-training. Code:
https://github.com/ZhentingWang/DUMP.