Demystifying Reinforcement Learning Post-Training of Language Models

2026-08-24

AI Summary

该研究通过解构强化学习后训练算法，探讨了其在提升大型语言模型能力时的机制和影响因素，如奖励信号、提示分布等，并分析了这些选择如何相互作用以影响后训练的成功。

Abstract

Reinforcement learning (RL) post-training has emerged as a powerful framework for enhancing the capabilities of large language models (LLMs), enabling impressive reasoning, math, and coding capabilities. Yet for many researchers and practitioners, the principles behind classical RL remain a "black box". In this work, we deconstruct the RL post-training algorithm, investigating each step to clarify what is actually happening beneath the surface. By isolating the mechanics of RL with Verifiable Rewards in a controlled and simplified environment, we examine how RL outcomes are shaped by the base model's prior distribution, the granularity of the reward signal, the diversity of the prompt distribution, and model scale. We use the entropy of the policy's output distribution as a lens to compare the distributions learned through pretraining, SFT, and RL post-training, revealing how each stage shapes model certainty. Our investigation sheds light on how these choices interact to affect post-training success. For example, we show that the effect of so-called 'spurious rewards' depends on the prompt distribution used for post-training. We also provide insight into why the success of RL post-training depends on whether the base model already places sufficient probability mass on the desired behavior, linking it to the classical concept of exploration in RL. Ultimately, we provide this primer as a resource to those in the NLP community wishing to incorporate RL as a tool in their toolbox.

Problem

• Reinforcement Learning
• Language Models
• Post-Training
• Reward Signal
• Prompt Distribution

Innovation

• Reinforcement Learning
• Post-Training
• Entropy of Policy Distribution
• Prompt Distribution
• Spurious Rewards