RosettaSim adapts frozen LLMs via structured autoregressive modeling of scene topology and agent states to reach SOTA short- and long-term traffic simulation on WOSAC, paired with RTE evaluation that correlates better with human-like fidelity.
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Self-play DAgger training in a batched pixel renderer produces end-to-end driving policies that reach competitive performance on HUGSIM and NAVSIM-v2 after real-world adaptation and improve with more self-play compute.
Self-play RL regularized with 30 minutes of human data produces driving policies that coordinate with humans, training in 15 hours on one GPU with 2500x less data than imitation learning.
citing papers explorer
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Long-term Traffic Simulation via Structured Autoregressive Modeling
RosettaSim adapts frozen LLMs via structured autoregressive modeling of scene topology and agent states to reach SOTA short- and long-term traffic simulation on WOSAC, paired with RTE evaluation that correlates better with human-like fidelity.
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Scaling Self-Play for End-to-End Driving
Self-play DAgger training in a batched pixel renderer produces end-to-end driving policies that reach competitive performance on HUGSIM and NAVSIM-v2 after real-world adaptation and improve with more self-play compute.
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Human-like autonomy emerges from self-play and a pinch of human data
Self-play RL regularized with 30 minutes of human data produces driving policies that coordinate with humans, training in 15 hours on one GPU with 2500x less data than imitation learning.