Adaptive Rollout Allocation for Online Reinforcement Learning with Verifiable Rewards

ICLR 2026 Conference Submission24594 Authors

Keywords: Reinforcement Learning, Resource Allocation, Large Language model post training

TL;DR: VIP enhances rollout allocation in reinforcement learning for improved efficiency

Abstract: Sampling efficiency is a key bottleneck in reinforcement learning with verifiable rewards. Existing group-based policy optimization methods, such as GRPO, allocate a fixed number of rollouts for all training prompts. This uniform allocation implicitly treats all prompts as equally informative, and could lead to inefficient computational budget usage and impede training progress. We introduce VIP, a Variance-Informed Predictive allocation strategy that allocates a given rollout budget to the prompts in the incumbent batch to minimize the expected gradient variance of the policy update. At each iteration, VIP uses a lightweight Gaussian process model to predict per-prompt success probabilities based on recent rollouts. These probability predictions are translated into variance estimates, which are then fed into a convex optimization problem to determine the optimal rollout allocations under a hard compute budget constraint. Empirical results show that VIP consistently improves sampling efficiency and achieves higher performance than uniform or heuristic allocation strategies in multiple benchmarks.

Primary Area: reinforcement learning

Submission Number: 24594