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

TL;DR

Gradient-based data valuation using TracIn scoring creates effective curricula for training game-theoretic motion planners, significantly improving performance on the nuPlan benchmark.

Contribution

This work introduces gradient-based data valuation with TracIn scores to enhance curriculum learning in motion planning, outperforming metadata-based heuristics.

Findings

TracIn-weighted curriculum reduces planning ADE to 1.704m.

Gradient scores are nearly orthogonal to scenario metadata.

Full-data curriculum weighting outperforms hard data selection.

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\% subsets degrade performance by $2\times$, 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.