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Achieve real-time autonomous driving policy generation with a new flow-matching RL algorithm that slashes inference latency without sacrificing performance.
MLLMs that ace simple traffic rules still struggle when multiple rules interact, especially when they conflict, revealing a critical gap in their ability to handle real-world driving complexity.
A mere 0.01% of tokens can destabilize LLM reinforcement learning, but masking their gradient updates unlocks significant performance gains.
