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REARL, a closed-loop simulation enhancement framework that integrates real traffic data with large language model (LLM) and modulates the simulated vehicle with reference to that real action, is proposed.
LLMs can power better local search, but only if you ground them geographically, align training with inference, and aggressively prune the vocabulary for speed.
Forget GPT-4o, the secret to better robot manipulation might be an agentic framework that generates diverse, physically plausible tasks, leading to superior VLA pre-training.
