Run High-Throughput Reinforcement Learning Training with End-to-End FP8 Precision

As LLMs transition from simple text generation to complex reasoning, reinforcement learning (RL) plays a central role. Algorithms like Group Relative Policy Optimization (GRPO) power this transition, enabling reasoning-grade models to continuously improve through iterative feedback. Unlike standard supervised fine-tuning, RL training loops are bifurcated into two distinct, high-intensity phases: a generation phase with a stringent latency requirement and a training phase requiring high throughput. To make these workloads viable, researchers and engineers are turning to low-precision datatypes like FP8 to boost performance in training and throughput-oriented generation. Moreover, in some scenarios where generation is bound by GPU memory bandwidth, using low-precision parameters can improve performance due to fewer bytes per parameter. This post dives deep into the systemic challenges of low-precision RL and how NVIDIA NeMo RL—an open source library within the NVIDIA NeMo framework—speeds up RL workloads while…