GSPO (Mixture-of-experts routing): superseded — cited as a baseline and beaten by newer methods. 1 paper(s) critique it, 3 beat it on benchmarks — #52 of 1370 most-superseded. Sub-problem: cluster led by Mixtral. Newer alternatives in the same sub-problem include PADD, MESA, PR2, RouteHijack, MASCing.

Mixture-of-experts routing

GSPO

Superseded baseline#52 of 1,370 most-superseded

Superseded — cited as a baseline and beaten by newer methods

1 papers critique it · 3 beat it on benchmarks

What papers say

Verbatim critique sentences, each from a paper that cites GSPO as a baseline.

However, these methods do not explicitly control the impact of routing drift on off-policy updates.

— Towards Stable and Effective Reinforcement Learning for Mixture-of-Experts

Beaten on benchmarks

Head-to-head results where a newer method reports beating GSPO. Values are copied from the source paper's tables — verify against the cited paper.

GRPO + \method beats GSPO
46.80 vs 35.76
Average · [Off-2]
PR2: Predictive Routing Replay for MoE-Based LLM Reinforcement Learning
GMPO+RS (RSPO) beats GSPO
85.2 vs 80.4
Pass@1 · [Code]
Towards Stable and Effective Reinforcement Learning for Mixture-of-Experts

What to use instead

Recent methods in the same sub-problem, not yet superseded in the knowledge base — arXiv benchmark leaders, not vetted production recommendations.