Microsoft's DiVeR Trains Verifiers on Decision-Critical States for VLA Scaling

Microsoft Research proposes DiVeR, which trains verifiers for vision-language-action models by weighting sparse decision-critical states, improving verifier-guided test-time scaling and task success rates.

2026-10-07 ~ 2026-10-07 · 2 related posts

• Microsoft's DiVeR Reweights VLA Verifier Learning Toward Decision-Critical States — MicrosoftResearch · 2026-10-07
• DiVeR preprint trains VLA verifiers only on decision-critical states for test-time scaling — SharonYixuanLi · 2026-10-07