Fudan’s Jia Xiaosong Argues Training Methods May Limit Autonomous Driving More Than Architecture

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Fudan University’s Jia Xiaosong argued in a Leiphone interview that training methods may now constrain end-to-end autonomous driving more than model architecture, at least on some academic benchmarks. He advocated imitation learning followed by reinforcement learning, framing how models are trained as a potentially greater bottleneck than their perception or cognition capabilities. Jia said world models should be judged by their usefulness for training, evaluation, and decision-making. He also sees similar challenges emerging in robotics, where complex contact physics makes simulation harder.

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Latest development10/19 11:06 (UTC+8)

Fudan’s Jia Xiaosong argues training, not VLA or WAM architecture, is the bottleneck 雷锋网

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10/19

11:06
雷锋网
Fudan’s Jia Xiaosong argues training, not VLA or WAM architecture, is the bottleneck
In a Leiphone interview, Fudan University’s Jia Xiaosong argues that training methods may constrain autonomous driving more than model architecture on some academic benchmarks. Drawing on his research, he advocates imitation learning followed by reinforcement learning and evaluates world models by their usefulness for training, evaluation, and decisions. He sees similar challenges emerging in robotics, where complex contact physics makes simulation harder.

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