IRL-VLA fine-tunes a vision-language-action driving policy with PPO against a learned reward world model trained on NAVSIM's EPDMS metrics, reaching 74.9 EPDMS on navhard-real.
Navsim: Data-driven non-reactive autonomous vehicle simulation and benchmarking
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IRL-VLA: Training an Vision-Language-Action Policy via Reward World Model
IRL-VLA fine-tunes a vision-language-action driving policy with PPO against a learned reward world model trained on NAVSIM's EPDMS metrics, reaching 74.9 EPDMS on navhard-real.