Microsoft(US:MSFT) 3 6 Ke·2025-09-01 04:38Group 1 - The core concept of the article revolves around the evolution of post-training methods in large language models, particularly focusing on the GRPO algorithm as a significant advancement in reinforcement learning paradigms [2][46]. - GRPO has emerged as a universal reinforcement learning algorithm applicable to a wide range of post-training tasks, with notable improvements over previous methods like PPO [2][48]. - The article discusses the importance of post-training in enhancing the adaptability and flexibility of models, addressing the limitations of pre-training alone [5][46]. Group 2 - The article highlights the transition from PPO to GRPO, emphasizing the reduction of computational costs and memory requirements, making GRPO a more efficient alternative [18][14]. - GRPO's methodology involves using historical performance data to establish a baseline for advantage estimation, eliminating the need for a separate value function [16][14]. - Despite its advantages, GRPO still faces stability issues, prompting further research and development of improved algorithms like DAPO and GSPO [19][48]. Group 3 - DAPO, developed by ByteDance and Tsinghua AIR, builds upon GRPO by introducing enhancements such as Clip-Higher and dynamic sampling to improve training efficiency [20][21]. - GSPO represents a significant advancement by shifting the focus from token-level to sequence-level importance sampling, which enhances training stability [28][30]. - GFPO addresses the limitations of GRPO by allowing for the simultaneous optimization of multiple response attributes, thus improving the overall performance of models [33][34].