Have been talking with people deploying foundational models/VLAs, + reading papers trying to increase real-world succes. Real world RL methods result in highest robust succes rates compared to any other approach. It’s pretty simple, practical bottlenecks aren’t in the pre-training anymore, it’s in the post-training, mostly using real world RL.

When you try simulation, you’ll suffer from sim2real gap, even with DR you’ll find yourself tuning the model again with real world data