Fudan Assistant Professor Jia Xiaosong: The Bottleneck May Not Be Model Architecture, But the Imitation Learning Objective Function

Around 2022, Jia Xiaosong ran an experiment in the CARLA simulation environment that changed the direction of his judgment. He and his team had collected what they themselves considered nearly unlimited driving data, and with 100 TB of storage and substantial compute resources, they trained larger models. The data volume had grown to the point where quantity was basically no longer a concern. But the results disappointed him: as data and model scale increased, driving capability did not achieve a leap from "driving adequately" to "driving perfectly"—various problems still appeared.

He later concluded that the most fundamental problem may lie in the training objective of imitation learning itself. Imitation learning optimizes how closely the model's behavior matches human demonstrations, not whether the decision is actually good. He gave an example from simulation: even with large amounts of added data, the model struggled to learn "stop at red, go at green" perfectly, because it could easily find a simpler rule—when the car ahead moves, I move. That single rule already drove training loss down very low across the vast majority of samples, leaving the model with little incentive to optimize the remaining rare but important cases. What it learned were correlations in the data, not necessarily the true basis for decision-making.