The Impact of Quantization on Large Reasoning Model Reinforcement Learning

• URL: http://arxiv.org/abs/2511.15694v1
• Date: Wed, 19 Nov 2025 18:50:58 GMT
• Title: The Impact of Quantization on Large Reasoning Model Reinforcement Learning
• Authors: Medha Kumar, Zifei Xu, Xin Wang, Tristan Webb,
• Abstract summary: Large-scale reinforcement learning (RL) can achieve strong reasoning capabilities without supervised fine-tuning.<n>Although post-training quantization (PTQ) and quantization-aware training (QAT) are well studied, how quantization impacts RL in large reasoning models (LRMs) remains an open question.<n>Our findings suggest that quantization-aware RL training negatively impacted the learning process, whereas PTQ and QLoRA led to greater performance.
• Score: 3.0443465826145637
• License: http://creativecommons.org/licenses/by/4.0/
• 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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