Group 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].

Core Viewpoint - The article discusses the evolution and significance of the Group Relative Policy Optimization (GRPO) algorithm in the context of large language models and reinforcement learning, highlighting its advantages and limitations compared to previous methods like Proximal Policy Optimization (PPO) [4][38]. Summary by Sections Development of Large Language Models - The rapid advancement of large language models has led to the emergence of various post-training methods, with GRPO being a notable innovation that enhances reinforcement learning paradigms [3][5]. Post-Training and Reinforcement Learning - Post-training is crucial for refining models' capabilities in specific domains, enhancing adaptability and flexibility to meet diverse application needs [12][11]. - Reinforcement learning, particularly through human feedback (RLHF), plays a vital role in the post-training phase, aiming to optimize model outputs based on user preferences [14][19]. GRPO and Its Advantages - GRPO eliminates the need for a separate critic model, reducing memory and computational costs significantly compared to PPO, which requires dual networks [30][35]. - The GRPO framework utilizes historical performance data to establish a baseline for evaluating model improvements, thus simplifying the training process [34][35]. Comparison of GRPO and PPO - GRPO offers substantial improvements in memory requirements and training speed, making it a more efficient choice for large language model training [37]. - Despite its advantages, GRPO still faces stability issues similar to those of PPO, particularly in smaller-scale reinforcement learning tasks [39]. Recent Innovations: DAPO, GSPO, and GFPO - DAPO introduces enhancements to GRPO, such as Clip-Higher and dynamic sampling, to address practical challenges encountered during training [41][42]. - GSPO advances the methodology by shifting the focus from token-level to sequence-level importance sampling, significantly improving training stability [48][49]. - GFPO allows for simultaneous optimization of multiple response attributes, addressing limitations of GRPO related to scalar feedback and multi-round reasoning tasks [61][63]. Conclusion - The evolution of post-training methods, from PPO to GRPO and beyond, illustrates a clear trajectory in optimizing large language models, with GRPO serving as a pivotal point for further advancements in the field [81][82].

Core Viewpoint - The article discusses the introduction of a new reinforcement learning algorithm called Group Filtered Policy Optimization (GFPO), which aims to enhance the efficiency of reasoning models by significantly reducing unnecessary token lengths during inference while maintaining accuracy [2][3][9]. Summary by Sections Introduction to GFPO - GFPO is a revolutionary algorithm that balances computational costs during training and testing phases, achieving up to an 80% reduction in token length during inference [3][5]. Background on GRPO - The article explains the Group Relative Policy Optimization (GRPO) as a simplified version of the Proximal Policy Optimization (PPO) algorithm, which does not require a value model for baseline advantage estimation [7][8]. - GRPO has limitations due to its reliance on a single scalar reward signal, making it challenging to optimize multiple response attributes simultaneously, leading to increased response lengths [8][9]. Mechanism of GFPO - GFPO allows targeted strategy optimization for desired response attributes by sampling a larger candidate response group and filtering based on specific characteristics [11]. - The algorithm normalizes the advantages of selected responses using their average and standard deviation, ensuring that only the most relevant responses are considered for policy updates [13][14]. Adaptive Difficulty in GFPO - An adaptive variant of GFPO is introduced, which allocates more training signals to harder problems, dynamically adjusting the number of retained responses based on problem difficulty [21][22]. Experimental Findings - The article presents various experimental findings, including: - The importance of sampling more responses to reduce response lengths effectively [28]. - Token efficiency optimization leads to significant length reductions while maintaining accuracy, with reductions of 70.9% to 84.6% across different benchmarks [31]. - GFPO effectively mitigates out-of-distribution length inflation while slightly improving accuracy [32]. - The adaptive difficulty variant outperforms the Shortest-k algorithm in length reduction across multiple benchmarks [31][40]. Conclusion - GFPO demonstrates a substantial reduction in unnecessary response lengths during reasoning and validation phases, achieving a 94.4% reduction in excess length for answers and a 66.7% reduction for validation steps in specific benchmarks [44].