[2604.00388] Gradient-Based Data Valuation Improves Curriculum Learning for Game-Theoretic Motion Planning

Computer Science > Machine Learning arXiv:2604.00388 (cs) [Submitted on 1 Apr 2026] Title:Gradient-Based Data Valuation Improves Curriculum Learning for Game-Theoretic Motion Planning Authors:Shihao Li, Jiachen Li, Dongmei Chen View a PDF of the paper titled Gradient-Based Data Valuation Improves Curriculum Learning for Game-Theoretic Motion Planning, by Shihao Li and 2 other authors View PDF HTML (experimental) Abstract: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 $1.704\pm0.029$\,m, significantly outperforming the metadata-based interaction-difficulty curriculum ($1.822\pm0.014$\,m; paired $t$-test $p=0.021$, Cohen's $d_z=3.88$) while exhibiting lower variance than the uniform baseline ($1.772\pm0.134$\,m). Our analysis reveals that TracIn scores and scenario metadata are nearly orthogonal (Spearman $\rho=-0.014$), 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\% su...