# 🐯 Liger GRPO meets TRL

DevFeed: [🐯 Liger GRPO meets TRL](<https://devfeed.tech/articles/liger-grpo-meets-trl-7332.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/liger-grpo>)

Author: Shivam Sahni; Kashif Rasul; Salman Mohammadi; Shirin Yamani; Yanning Chen; Liberty

Published: 2025-05-25T00:00:00Z

Content type: tutorial

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [grpo](<https://devfeed.tech/topics/grpo.md>), [trl](<https://devfeed.tech/topics/trl.md>), [Reinforcement learning](<https://devfeed.tech/topics/reinforcement-learning.md>), [Fine-tuning](<https://devfeed.tech/topics/fine-tuning.md>), [Optimization](<https://devfeed.tech/topics/optimization.md>), [GPU](<https://devfeed.tech/topics/gpu.md>), [LLMs](<https://devfeed.tech/topics/llms.md>), [rlhf](<https://devfeed.tech/topics/rlhf.md>), [coding](<https://devfeed.tech/topics/coding.md>), [math](<https://devfeed.tech/topics/math.md>)

Tags: [coding](<https://devfeed.tech/tags/coding.md>), [fine-tuning](<https://devfeed.tech/tags/fine-tuning.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [grpo](<https://devfeed.tech/tags/grpo.md>), [large-language-models-llms](<https://devfeed.tech/tags/large-language-models-llms.md>), [liger](<https://devfeed.tech/tags/liger.md>), [llm](<https://devfeed.tech/tags/llm.md>), [optimization](<https://devfeed.tech/tags/optimization.md>), [partnership](<https://devfeed.tech/tags/partnership.md>), [performance](<https://devfeed.tech/tags/performance.md>), [reinforcement-learning](<https://devfeed.tech/tags/reinforcement-learning.md>), [rlhf](<https://devfeed.tech/tags/rlhf.md>), [trl](<https://devfeed.tech/tags/trl.md>)

## AI overview

This article explains how Group Relative Policy Optimization (GRPO) can reduce the resource requirements of reinforcement learning fine-tuning for language models. It presents a TRL optimization based on chunked GRPO loss that reduces peak memory usage by 40% and discusses scaling GRPO across multiple GPUs and nodes while preserving performance and correctness.

## Source excerpt

We're on a journey to advance and democratize artificial intelligence through open source and open science.