Real-World Humanoid RL: LIFT’s Pretraining and On-Device Fine‑Tuning Paradigm

The paper presents LIFT, a framework that combines large‑scale off‑policy SAC pretraining with physics‑informed world‑model fine‑tuning to enable safe, sample‑efficient continual reinforcement learning on real humanoid robots, demonstrating zero‑sample deployment and rapid adaptation across diverse speed distributions.

LIFTSACcontinuous learning

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Real-World Humanoid RL: LIFT’s Pretraining and On-Device Fine‑Tuning Paradigm