QeRL: Beyond Efficiency -- Quantization-enhanced Reinforcement Learning
  for LLMs
Authors: name: Wei Huang, name: Yi Ge, name: Shuai Yang, name: Yicheng Xiao, name: Huizi Mao, name: Yujun Lin, name: Hanrong Ye, name: Sifei Liu, name: Ka Chun Cheung, name: Hongxu Yin, name: Yao Lu, name: Xiaojuan Qi, name: Song Han, name: Yukang Chen
Organization: NVIDIA

We propose QeRL, a Quantization-enhanced Reinforcement Learning framework for
large language models (LLMs). While RL is essential for LLMs' reasoning
capabilities, it is resource-intensive, requiring substantial GPU memory and
long rollout durations. QeRL addresses these issues by combining NVFP4
quantization with Low-Rank Adaptation (LoRA), accelerating rollout phase of RL
while reducing memory overhead. Beyond efficiency, our findings show that
quantization noise increases policy entropy, enhancing exploration, and
enabling the discovery of better strategies during RL. To further optimize
exploration, QeRL introduces an Adaptive Quantization Noise (AQN) mechanism,
which dynamically adjusts noise during training. Experiments demonstrate that
QeRL delivers over 1.5 times speedup in the rollout phase. Moreover, this is
the first framework to enable RL training of a 32B LLM on a single H100 80GB
GPU, while delivering overall speedups for RL training. It also achieves faster
reward growth and higher final accuracy than 16-bit LoRA and QLoRA, while
matching the performance of full-parameter fine-tuning on mathematical
benchmarks such as GSM8K (90.8%) and MATH 500 (77.4%) in the 7B model. These
results establish QeRL as an efficient and effective framework for RL training
in LLMs.