Generalist robot policies can self-improve on many tasks at once in deployment using RL, and this multi-task improvement can transfer to unseen tasks—but only with the right algorithmic recipe combining value-based RL and careful task distribution choices.
This paper shows that large pre-trained robot models can improve themselves on multiple tasks simultaneously through reinforcement learning during deployment, rather than just adapting to single tasks.