The Impact of Quantization on Large Reasoning Model Reinforcement Learning

Medha Kumar; Zifei Xu; Xin Wang; Tristan Webb

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arXiv · 2511.15694

The Impact of Quantization on Large Reasoning Model Reinforcement Learning

Medha Kumar·Zifei Xu·Xin Wang·Tristan Webb

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Abstract

Strong reasoning capabilities can now be achieved by large-scale reinforcement learning (RL) without any supervised fine-tuning. Although post-training quantization (PTQ) and quantization-aware training (QAT) are well studied in the context of fine-tuning, how quantization impacts RL in large reasoning models (LRMs) remains an open question. To answer this question, we conducted systematic experiments and discovered a significant gap in reasoning performance on mathematical benchmarks between post-RL quantized models and their quantization-aware RL optimized counterparts. Our findings suggest that quantization-aware RL training negatively impacted the learning process, whereas PTQ and QLoRA led to greater performance.

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Medha Kumar, Zifei Xu, Xin Wang, Tristan Webb. 2025-11-19. The Impact of Quantization on Large Reasoning Model Reinforcement Learning. https://arxiv.org/abs/2511.15694

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