Prism-GRPO: Faster VLA Policy Optimization via Splitting Same-outcome Groups
GRPO is increasingly used for reinforcement learning of vision-language-action (VLA) policies because, unlike PPO, it does not require training a critic. This simplification comes with a sampling cost: group-relative advantages require multiple rollouts from each scene. Under binary success rewards, groups whose rollouts all succeed or all fail have zero advantage and are discarded by dynamic sampling. These groups are especially common early in training, when most rollouts fail, wasting much of the expensive robotic rollout budget. We introduce Prism-GRPO, which augments binary outcome reward with a weighted trajectory-level execution-quality score. By splitting same-outcome groups into ...
| Authors | Zeyun Deng, Yuzhe Lu, Yawei Wang et al. |
|---|---|
| Primary category | cs.RO |
| Published | 18 August 2026 |
| Last updated at source | 18 August 2026 |
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