Group Sequence Policy Optimization (GSPO); A Smarter Approach to RL for LLMs and MoE Models

Just read the Group Sequence Policy Optimization (GSPO) paper from the Qwen team, and it seems like a smart solution to the instability of reinforcement learning for LLMs, especially with Mixture-of-Experts (MoE) models.

The paper argues that previous methods, like GRPO, were going about it the wrong way. They were looking at things token-by-token, which introduces a ton of noise and makes everything fragile.

Group Sequence Policy Optimization (GSPO) — a reinforcement learning algorithm for building more stable and powerful large language models! Instead of focusing on individual tokens, it evaluates and rewards the entire generated sequence. It’s a shift from “is this a good next word?” to “is this a good overall response?”.

The Qwen team’s new method tackles the critical instability issues that cause model collapse during large-scale RL training.

⚡ Sequence-Level Optimization

GSPO moves away from noisy token-level updates. By calculating importance ratios and rewards for the entire sequence, it aligns the optimization process with the reward signal, drastically improving training stability.

🚀 Unlocks Stable MoE Training

It inherently solves the expert-activation volatility in Mixture-of-Experts (MoE) models. This eliminates the need for complex workarounds and enables stable, efficient RL training for these powerful sparse models.

📊 Superior Performance & Efficiency

GSPO demonstrates significantly better training efficiency and benchmark performance compared to previous algorithms like GRPO, contributing directly to the major improvements in the latest Qwen3 models.

🔧 Simplifies RL Infrastructure

Its robustness to precision differences paves the way for a streamlined RL pipeline, potentially allowing direct use of data from inference engines and reducing computational overhead.

Kudos to the Qwen Team at Alibaba Inc.

Definitely worth a read if you’re in the LLM training space.

We do not grow absolutely, chronologically. We grow sometimes in one dimension, and not in another, unevenly. We grow partially. We are relative. We are mature in one realm, childish in another. —Anais Nin

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