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.…