QeRL is a quantization-enhanced reinforcement learning framework for large language models that addresses the resource intensity of RL training. It combines NVFP4 quantization with Low-Rank Adaptation (LoRA) to accelerate the rollout phase of RL and reduce memory overhead, enabling training of 32B parameter models on consumer-grade hardware.