Gradient-Based Data Valuation Improves Curriculum Learning for Game-Theoretic Motion Planning

We demonstrate that gradient-based data valuation produces curriculum orderings that significantly outperform metadata-based heuristics for training game-theoretic motion planners. Specifically, we apply TracIn gradient-similarity scoring to GameFormer on the nuPlan benchmark and construct a curriculum that weights training scenarios by their estimated contribution to validation loss reduction. Across three random seeds, the TracIn-weighted curriculum achieves a mean planning ADE of 1.704±0.029,m, significantly outperforming the metadata-based interaction-difficulty curriculum (1.822±0.014,m; paired t-test p=0.021, Cohen's dz=3.88) while exhibiting lower variance than the uniform baseline (1.772±0.134,m). Our analysis reveals that TracIn scores and scenario metadata are nearly orthogonal (Spearman ρ=-0.014), indicating that gradient-based valuation captures training dynamics invisible to hand-crafted features. We further show that gradient-based curriculum weighting succeeds where hard data selection fails: TracIn-curated 20% subsets degrade performance by 2×, whereas full-data curriculum weighting with the same scores yields the best results. These findings establish gradient-based data valuation as a practical tool for improving sample efficiency in game-theoretic planning.