TMRL Improves Robot Policy Training
abhishekunique7 · x · 2026-07-10
A repost introduces TMRL: the author argues that RL cannot teach models to solve problems they have never encountered before, a limitation that also applies to robotic policies. This method injects diffusion noise into the policy during the pre-training phase to broaden the action distribution, thereby leaving room for exploration in subsequent RL. It also claims to enable VLA fine-tuning on a real robot within one hour.
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