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arXiv · 2608.13026

Temporal GRPO: Beyond Trajectory-Level Credit in Vision-Language-Action Reinforcement Learning

Yao Zhou·Hang Gao·Fengge Wu·Changwen Zheng·Wenwen Qiang
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Abstract

Outcome-driven reinforcement learning offers a scalable way to post-train vision-language-action (VLA) policies from sparse task-success feedback. In common GRPO-based VLA post-training, one rollout-level advantage is applied to every action in the trajectory. A rollout that completes several valid stages but fails later can therefore penalize the actions that produced its earlier progress. We call this trajectory-level credit aliasing. Temporal GRPO addresses this problem by constructing detectable task stages, aligning each rollout with stage-specific action intervals, and comparing only rollouts that have entered the same stage. The resulting stage advantages are applied to their corresponding intervals in a single policy update. On RoboTwin 2.0, Temporal GRPO improves task success and sample efficiency, with consistent gains across task horizons. Controlled updates on LIBERO-Long preserve shared prerequisite stages and concentrate improvement at the first stage where rollout outcomes diverge.

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Yao Zhou, Hang Gao, Fengge Wu, Changwen Zheng, Wenwen Qiang. 2026-08-13. Temporal GRPO: Beyond Trajectory-Level Credit in Vision-Language-Action Reinforcement Learning. https://arxiv.org/abs/2608.13026

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