Where Groups Split: A Probe Prior for Nonzero Within-Group Variance in RL Post-Training
Group relative policy optimization learns from a prompt only when the rewards in the sampled group disagree. With a binary verifier, a group that is entirely correct or entirely incorrect has within-group variance zero and a zero advantage. Uniform sampling continues to spend rollouts on prompts that are unanimous now and stay unanimous as training goes on. Pass rate is a weak forecast of that future. Two prompts at the same pass rate can move apart: one into the range where a finite group splits, and one toward a ceiling where the group becomes unanimous again. Aperture estimates the later pass rate from a short probe. A small subset of the pool is trained with the same recipe, every pro...
| Authors | Jonah Ellison, Mateo Ruiz, Owen Pell et al. |
|---|---|
| Primary category | cs.LG |
| Published | 18 October 2026 |
| Last updated at source | 18 October 2026 |
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