Temporal GRPO: Beyond Trajectory-Level Credit in Vision-Language-Action Reinforcement Learning
A novel reinforcement learning approach that eliminates trajectory-level bias for more precise robotic skill acquisition.
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 comp...
ArXiv CS.RO
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Temporal GRPO solves the credit assignment problem by decoupling action rewards from entire trajectory failures, allowing models to learn from partially successful steps. This refinement significantly accelerates policy convergence in complex robotic tasks by isolating where and why a specific sequence succeeds or fails.
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