Definition
Plain language
A reinforcement-learning variant that grades a whole generated sequence rather than each token.
As stated in the literature
Group Sequence Policy Optimization, a sequence-level adaptation of GRPO that avoids per-token gradient noise; useful for mixture-of-experts models where token-level routing makes per-token signals unstable.
Why it matters: For mixture-of-experts models where routing makes token-level gradients noisy, sequence-level optimization is often the difference between stable and unstable training.
For example, GSPO scores each whole generated solution as one unit and updates the policy from the sequence-level reward, rather than tying every token to its own gradient signal.
Heard on the show
“One is the RL objective itself — GSPO.”Episode 189 — Why Phone Agents Ace the Test and Crash on Your Actual Phone