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

**URL:** <https://forums.developer.nvidia.com/t/run-high-throughput-reinforcement-learning-training-with-end-to-end-fp8-precision/367245>\
**Category:** Technical Blog\
**Created:** [April 20, 2026, 10:52pm UTC](https://forums.developer.nvidia.com/t/run-high-throughput-reinforcement-learning-training-with-end-to-end-fp8-precision/367245 "2026-04-20T22:52:43Z")\
**Posts on this page:** 1\
**Page:** 1

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**Author:** ![jwitsoe](https://sea2.discourse-cdn.com/nvidia/user_avatar/forums.developer.nvidia.com/jwitsoe/32/16591_2.png) [@jwitsoe](https://forums.developer.nvidia.com/u/jwitsoe)\
**Post date:** [April 20, 2026, 10:52pm UTC](https://forums.developer.nvidia.com/t/run-high-throughput-reinforcement-learning-training-with-end-to-end-fp8-precision/367245/1 "2026-04-20T22:52:43Z")

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Originally published at: [Run High-Throughput Reinforcement Learning Training with End-to-End FP8 Precision | NVIDIA Technical Blog](https://developer.nvidia.com/blog/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…