After DPO, the typical research trajectory moves through GRPO, DAPO, GSPO, and SAPO, each introducing new optimization objectives, sampling strategies, and reward‑shaping techniques that aim to reduce memory usage, improve gradient stability, and enhance the efficiency of large‑model reinforcement learning.
GSPO (Group Sequence Policy Optimization) is a reinforcement‑learning algorithm for LLMs that replaces token‑level GRPO with sequence‑level optimization, addressing instability in ultra‑large model training, especially for long‑sequence and MoE architectures, by aligning reward granularity and reducing variance.
This article provides a comprehensive technical analysis of PPO‑based reinforcement learning methods for large language models, detailing the evolution from the original PPO algorithm through GRPO, DAPO, and GSPO, and explaining their motivations, mathematical formulations, advantages, and practical challenges such as entropy collapse and importance‑sampling variance.
This article introduces the GSPO (Group Sequence Policy Optimization) reinforcement learning algorithm, explains its advantages over GRPO, and provides a detailed, end‑to‑end tutorial for reproducing GSPO training on Alibaba Cloud's PAI platform using the PAI‑ChatLearn framework.
The paper identifies that GRPO’s token‑level importance weighting introduces high‑variance noise causing instability in large‑scale language model RL training, and proposes GSPO, a sequence‑level importance sampling method that aligns with reward definitions, improves gradient stability, and yields higher training efficiency and better performance across benchmarks.
