AutoPrune: Accelerating End-to-End Driving Training via Gradient-Preserving Adaptive Data Pruning

Unified end-to-end architectures have reshaped autonomous driving, but their reliance on massive datasets leads to weeks-long training, severely hindering model iteration. A natural way to accelerate training is to remove simple or redundant data that provide limited learning value. However, existing data reduction methods for end-to-end driving often rely on static data features and hand-crafted selection rules, facing two fundamental limitations: (1) Gradient distortion. Pruning low-value data can alter the gradient composition and degrade model performance; (2) Training-dynamics misalignment. Evolving data values make fixed pruning decisions stale, while fewer iterations can misalign the optimization schedule with full-data progress. Therefore, we present AutoPrune, which reduces training-data usage to accelerate end-to-end driving training while mitigating pruning-induced performance degradation. First, Gradient-Preserving Data Pruning is introduced to compensate for the missing training gradients caused by pruning low-loss samples, so that the corrected aggregate gradient matches its full-data counterpart in expectation. Second, Training-Dynamics Adaptation uses loss-adaptive sample gating to update pruning decisions from the latest available loss records and pruning-aware learning-rate alignment to follow the corresponding full-data training progress. Experiments across multiple end-to-end driving models show that AutoPrune retains performance close to the corresponding full-data references at a 40% overall pruning ratio. Comparative and ablation studies further support the effectiveness of the proposed components.