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

2026-03-31

AI Summary

This work addresses the limitation of conventional metadata-based curriculum learning in identifying training scenarios critical for improving motion planning performance in interactive driving tasks. For the first time, it introduces gradient-driven data valuation (TracIn) to this domain, constructing a curriculum by quantifying each training sample’s contribution to validation loss reduction. The resulting curriculum is shown to be nearly orthogonal to handcrafted metadata, capturing dynamic training effects that metadata overlooks. Evaluated on the GameFormer architecture using the nuPlan benchmark, the proposed approach achieves an average ADE of 1.704 ± 0.029 meters under curriculum-weighted training, significantly outperforming metadata-based curriculum learning (1.822 ± 0.014 meters, p = 0.021) and exhibiting lower variance than uniform sampling.

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 $ρ=-0.014$), 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 $2\times$, 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.

Problem

• curriculum learning
• game-theoretic motion planning
• data valuation
• sample efficiency
• training dynamics

Innovation

• gradient-based data valuation
• curriculum learning
• game-theoretic motion planning
• TracIn
• sample efficiency