Train Less, Learn More: Adaptive Efficient Rollout Optimization for Group-Based Reinforcement Learning

2026-02-15

AI Summary

This work addresses a critical inefficiency in population-based reinforcement learning methods such as GRPO, where homogeneous rollout outcomes cause the advantage function to vanish, leading to absent gradient signals and wasted computation. To mitigate this issue, the authors propose AERO, a novel approach that dynamically avoids zero-advantage regions through adaptive control of rollout counts, a selective rejection mechanism, and Bayesian posterior estimation. AERO maintains effective policy optimization while substantially improving training efficiency. Empirical results demonstrate that, under an identical rollout budget, AERO reduces total training computation by approximately 48% and decreases per-step runtime by 45%, while matching or surpassing GRPO in performance on both Pass@8 and Avg@8 evaluation metrics.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Multiagent Systems: Adversarial Agents
• Machine Learning: Reinforcement Learning

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Reinforcement learning (RL) plays a central role in large language model (LLM) post-training. Among existing approaches, Group Relative Policy Optimization (GRPO) is widely used, especially for RL with verifiable rewards (RLVR) fine-tuning. In GRPO, each query prompts the LLM to generate a group of rollouts with a fixed group size $N$. When all rollouts in a group share the same outcome, either all correct or all incorrect, the group-normalized advantages become zero, yielding no gradient signal and wasting fine-tuning compute. We introduce Adaptive Efficient Rollout Optimization (AERO), an enhancement of GRPO. AERO uses an adaptive rollout strategy, applies selective rejection to strategically prune rollouts, and maintains a Bayesian posterior to prevent zero-advantage dead zones. Across three model configurations (Qwen2.5-Math-1.5B, Qwen2.5-7B, and Qwen2.5-7B-Instruct), AERO improves compute efficiency without sacrificing performance. Under the same total rollout budget, AERO reduces total training compute by about 48% while shortening wall-clock time per step by about 45% on average. Despite the substantial reduction in compute, AERO matches or improves Pass@8 and Avg@8 over GRPO, demonstrating a practical, scalable, and compute-efficient strategy for RL-based LLM alignment.

Problem

• Reinforcement Learning
• Group Relative Policy Optimization
• Zero-Advantage
• Compute Efficiency
• LLM Alignment

Innovation

• Adaptive Rollout
• Selective Rejection
• Bayesian Posterior
• Zero-Advantage Mitigation
• Compute-Efficient RL