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Training self-driving policies directly from perspective images can rival traditional methods that rely on privileged data, achieving impressive zero-shot transfer performance.
Post-training on synthesized safety-critical scenarios can dramatically enhance the reliability of autonomous driving systems, reducing failures in rare but critical events.
MLLMs can get a surprising visual reasoning boost from a simple trick: adding just a dash of visually grounded self-supervision to instruction tuning.
