Hugging Face· Platform

🐯 Liger GRPO meets TRL

Liger supercharges TRL ’s Group Relative Policy Optimization GRPO Trainer by slashing memory usage by 40% with zero drop in model quality. We also added support for FSDP and PEFT , making it easier than ever to scale GRPO across multiple GPUs. Motivation Fine-tuning language models using reinforcement learning (RL) is a crucial step in a model's training lifecycle for steering models towards desirable behaviours which are more complex than can be achieved through typical supervised fine-tuning. RL has traditionally been applied to optimize large language models (LLMs) using the Proximal Policy Optimization (PPO) algorithm. This approach, often associated with Reinforcement Learning from Human Feedback (RLHF), utilizes a separately trained reward model to guide the fine-tuning of the primary model. …

Announced

25 May 2025 00:00 UTC

Detected

03 Aug 2026 21:12 UTC (+435d after)