1. Well, based on Karpathy's talks on Tesla FSD, his solution is to actually make the training set reflect everything you'd see in reality. The tricky part is that if something occurs 0.0000001% IRL and something else occurs 50% of the time, they both need to make 5% of the training corpus. The thing with multimodal LLMs is that lidar/depth input can just be another input that gets encoded along with everything else, so for driving "there's a blob I don't quite recognize" is still a blob you have to drive around.

2. Figure has a dual-model architecture which makes a lot of sense: A 7B model that does higher-level planning and control and a runs at 8Hz, and a tiny 0.08B model that runs at 200Hz and does the minute control outputs. https://www.figure.ai/news/helix