A Simple Explanation of GSPO

GSPO (Group Sequence Policy Optimization) is a reinforcement learning technique for training language models. Step through the visualization below to see how it works.

For a more detailed explanation, continue reading.

The Problem

Training a language model on open-ended tasks is tricky. There's no single correct answer, but some responses are better than others. Reinforcement learning addresses this by rewarding preferred outputs. Here's how it works with GSPO. First, we generate multiple responses.

Score and Rank

Next, a reward function scores each response. This could be a trained model, a set of rules, or human ratings. Scores are compared to the group average: above-average responses get reinforced, below-average ones get suppressed.

Comparison to GRPO

Everything so far is identical to GRPO (Group Relative Policy Optimization). Both methods assign the same advantage to all tokens in a sequence. The difference is in how they handle clipping.

Clipping limits how much a sample can influence training when it deviates from the current policy. GRPO clips at the token level, while GSPO clips at the sequence level. This sequence-level approach improves training stability and typically yields better results.

Wrapping Up

Thanks for reading! This was a simple overview of how GSPO trains language models through group-based reinforcement learning.

A technical deep dive with implementation details is planned. Join our mailing list to be notified when it's published.