DeepSeek的GRPO会导致模型崩溃？看下Qwen3新范式GSPO

Core Viewpoint - The article discusses the evolution of reinforcement learning techniques in the post-training phase of large language models (LLMs), highlighting the introduction of Group Sequence Policy Optimization (GSPO) as a solution to the instability issues associated with Group Relative Policy Optimization (GRPO) [2][10][31]. Group 1: Training Phases and Techniques - The training of large language models typically consists of two phases: pre-training and post-training, where the latter focuses on improving the model's understanding and execution of human instructions [1]. - The post-training phase employs reinforcement learning, with initial methods like Reinforcement Learning from Human Feedback (RLHF) being time-consuming and costly due to reliance on human annotators [2][3]. Group 2: Innovations and Comparisons - DeepSeek introduced an automated approach to RLHF, significantly reducing costs and improving efficiency by allowing the model to learn through reward signals rather than manual evaluations [2]. - The DeepSeek team proposed the Group Relative Policy Optimization (GRPO) algorithm, which they believe is more effective than the Proximal Policy Optimization (PPO) used by OpenAI in ChatGPT [3][5]. Group 3: Issues with GRPO - The Qwen team identified serious stability issues with GRPO, particularly due to its reliance on token-level importance sampling, which can lead to high variance and training instability [10][11][12]. - The instability arises from the incorrect application of importance sampling weights at the token level, which can accumulate high variance in long sequences, exacerbating the training challenges [15][16][17]. Group 4: Introduction of GSPO - To address the issues with GRPO, the Qwen team proposed the Group Sequence Policy Optimization (GSPO), which utilizes sequence-level importance sampling to enhance training stability [10][22][31]. - GSPO's design mitigates the accumulation of variance seen in token-level sampling, leading to improved training efficiency and stability [23][24]. Group 5: Experimental Evidence and Advantages - Experimental results demonstrated that GSPO outperformed GRPO in various tasks, showcasing better scalability and efficiency in training [20][30]. - The Qwen team highlighted that GSPO simplifies the training of Mixture-of-Experts (MoE) models by eliminating the need for auxiliary strategies like Routing Replay, which were necessary for GRPO to achieve stable convergence [25][27][30].