Over the last few months, we have gradually assembled the ingredients for generalist post-training of generalist policies.
In short: value estimation enables broader and more efficient improvement as task diversity scales.
Can RL post-training of generalist robot policies enable improvement not just on the tasks we train on, but also generalization beyond them, improving on novel tasks not seen in training?
We show that, with the proper RL recipe, we can enable such "generalist improvement"! 🧵
