NVIDIA researchers have introduced Jet-RL, a novel framework designed to accelerate the training of large language models through FP8 reinforcement learning. Standard methods often use lower precision only for the rollout phase, which creates a numerical mismatch that causes training instability and accuracy loss during complex tasks. Jet-RL solves this by enforcing a unified precision flow, ensuring that both the training and generation stages utilize consistent FP8 quantization. This approach significantly reduces computational bottlenecks, as the rollout phase typically accounts for over 70% of total training time. By implementing fine-grained quantization and optimized kernels, the framework achieves up to a 41% training speedup while maintaining the stability of traditional high-precision methods. Ultimately, the system allows for faster end-to-end development of reasoning models without sacrificing performance on difficult benchmarks. Source: January 27 2026 Jet-RL: Enabling On-Policy FP8 Reinforcement Learning with Unified Training and Rollout Precision Flow NVIDIA, MIT, UC Berkeley, Independent Researcher Haocheng Xi, Charlie Ruan, Peiyuan Liao, Yujun Lin, Han Cai, Yilong Zhao, Shuo Yang, Kurt Keutzer, Song Han, Ligeng Zhu https://arxiv.org/pdf/2601.14243