Efficient Process Reward Model Training via Active Learning
Authors: name: Keyu Duan, name: Zichen Liu, name: Xin Mao, name: Tianyu Pang, name: Changyu Chen, name: Qiguang Chen, name: Michael Qizhe Shieh, name: Longxu Dou

Process Reward Models (PRMs) provide step-level supervision to large language
models (LLMs), but scaling up training data annotation remains challenging for
both humans and LLMs. To address this limitation, we propose an active learning
approach, ActPRM, which proactively selects the most uncertain samples for
training, substantially reducing labeling costs. During training, we use the
PRM to estimate uncertainty after the forward pass, retaining only highly
uncertain data. A capable yet costly reasoning model then labels this data.
Then we compute the loss with respect to the labels and update the PRM's
weights. We compare ActPRM vs. vanilla fine-tuning, on a pool-based active
learning setting, demonstrating that ActPRM reduces 50% annotation, but
achieving the comparable or even better performance. Beyond annotation
efficiency, we further advance the actively trained PRM by filtering over 1M+
math reasoning trajectories with ActPRM, retaining 60% of the data. A
subsequent training on this selected dataset yields a new state-of-the-art
(SOTA) PRM on ProcessBench (75.0%) and PRMBench (65.5%) compared with same
sized models.