Towards Full Pipeline FP8 Reinforcement Learning for LLMs

Full-pipeline FP8 reinforcement learning causes training instability through compounded quantization noise that distorts importance ratios. This noise pushes negative-advantage tokens outside the trust region and erroneously zeros out gradients, leading to mid-training entropy surges and garbled outputs despite existing train-inference correction techniques.