ACRL: Adaptive Control of Training-Inference Discrepancy for Stable Reinforcement Learning

2026-07-27 · Source: Computation and Language · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, quick

Summary

Adaptive Control Reinforcement Learning (ACRL) is a novel method designed to stabilize Reinforcement Learning (RL) training for Large Language Models (LLMs) by addressing the inherent discrepancy between training and inference environments. This discrepancy arises from architectural separations and the use of low-precision quantization, such as FP8, during inference compared to higher-precision computation in training. ACRL adaptively manages this training-inference discrepancy, maintaining it within a reasonable range to ensure stable RL training. Beyond stabilization, ACRL intrinsically boosts policy entropy, which enhances exploration and improves overall accuracy. Experimental results demonstrate that ACRL consistently stabilizes RL training with FP8 quantization, matches the accuracy of BF16 baselines, and outperforms importance sampling (IS) fixes.

Key takeaway

For Machine Learning Engineers and AI Scientists developing Reinforcement Learning models for Large Language Models, especially when employing low-precision inference like FP8 quantization, ACRL offers a robust solution. Implementing ACRL can significantly stabilize your training processes, mitigate issues caused by training-inference discrepancy, and improve overall model accuracy by enhancing exploration. Consider integrating ACRL to achieve more reliable and performant LLM-based RL systems, potentially outperforming traditional importance sampling fixes.

Key insights

Adaptive control of training-inference discrepancy stabilizes RL training and enhances exploration.

Principles

• Training-inference discrepancy causes RL instability.
• Adaptive control stabilizes RL training.
• Maintaining discrepancy enhances policy exploration.

Method

ACRL adaptively controls the training-inference discrepancy within a reasonable range to stabilize RL training, inherently increasing policy entropy for enhanced exploration and improved accuracy.

In practice

• Implement ACRL for stable LLM RL training.
• Apply ACRL when using FP8 inference quantization.
• Consider ACRL as an alternative to importance sampling.

Topics

• Reinforcement Learning
• Large Language Models
• Training-Inference Discrepancy
• Quantization
• Policy Entropy
• Adaptive Control

Best for: Research Scientist, AI Engineer, AI Scientist, Machine Learning Engineer, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computation and Language.