Kai Zhang

Autonomous driving models can be made significantly more robust and safe by explicitly de-confounding their training via causal intervention, eliminating reliance on spurious correlations.

Forget hand-engineered reward functions: this method uses language models to learn factorized world states that generalize to new goals and environments, outperforming LLM-as-a-Judge in zero-shot reward prediction.