This article discusses the Group Relative Policy Optimization (GRPO) algorithm and its applications in training reasoning models using reinforcement learning (RL). It outlines common techniques to address GRPO's limitations and compares different RL training approaches, particularly focusing on Reinforcement Learning with Verifiable Rewards (RLVR).

This article explores the shift towards training AI models through reinforcement learning (RL) as text data sources diminish. It discusses the concept of intelligence involution, highlighting the rise of custom RL models and the implications for businesses in the next year. The text dives into technical aspects like GRPO and LoRA, addressing the challenges and opportunities in building specialized AI models.

Reinforcement Learning (RL) techniques, particularly the Group Relative Policy Optimization (GRPO) algorithm, have been utilized to significantly improve the mathematical reasoning capabilities of language models. The study highlights how proper infrastructure, data diversity, and effective training practices can enhance performance, while also addressing challenges like model collapse and advantage estimation bias.

Liger enhances TRL’s Group Relative Policy Optimization (GRPO) by reducing memory consumption by 40% during training without sacrificing model quality. The integration also introduces support for Fully Sharded Data Parallel (FSDP) and Parameter-Efficient Fine-Tuning (PEFT), facilitating scalable training across multiple GPUs. Additionally, Liger Loss can be paired with vLLM for accelerated text generation during training.