Follow the Mixed Groups: Ridge-Tracking Gaussian Sampling for Zero-Variance Web Rollouts
Group relative policy optimization learns from a question only when the sampled responses disagree. A group that is entirely correct or entirely incorrect has zero reward variance and a zero advantage. On the Web, that group has already spent its searches and page fetches. Gaussian curricula aim the next batch at pass rates where a finite group can still mix. A Gaussian on the latest empirical rate treats every all-fail history as the same point, and a center stepped by the average signed change of those rates is pushed by prompts already stuck at 0 or 1. RIDGE keeps the Gaussian curriculum and changes the coordinate, the center, and the width. Each prompt carries a Beta posterior. The sa...
| Authors | Tomas Duncun, Jonah Ellison, Owen Pell et al. |
|---|---|
| Primary category | cs.LG |
| Published | 18 October 2026 |
| Last updated at source | 18 October 2026 |
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