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

• URL: http://arxiv.org/abs/2604.00388v1
• Date: Wed, 01 Apr 2026 02:17:27 GMT
• Title: Gradient-Based Data Valuation Improves Curriculum Learning for Game-Theoretic Motion Planning
• Abstract summary: gradient-based data valuation produces curriculum orderings that significantly outperform metadata-based subsets for training gametheoretic motion planners.<n> gradient-based data valuation as a practical tool for improving sample efficiency in game-theoretic planning.
• Score: 9.553350856191743
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Selectivity Drives Efficiency: Dataset Pruning for Visual Place Recognition [52.52131290631774]
  We propose a place-wise dataset pruning framework tailored for visual place recognition (VPR)<n>Our method treats each place as the basic pruning unit and introduces two novel metrics, i.e., intra-place diversity (IPD) and inter-place similarity (IPS)<n>Experiments demonstrate that our method consistently outperforms state-of-the-art DP baselines under different pruning ratios.
  arXiv  Detail & Related papers  (2026-07-16T12:16:00Z)
• Convex Dataset Valuation for Post-Training [21.22512177964122]
  We propose a scalable convex dataset-level valuation method based on mean matching (KMM) in gradient space.<n>We show that our approach consistently outperforms existing valuation baselines, achieving stronger performance with low overhead.
  arXiv  Detail & Related papers  (2026-05-15T23:35:07Z)
• GradAlign: Gradient-Aligned Data Selection for LLM Reinforcement Learning [55.03441672267886]
  We propose GradAlign, a gradient-aligned data selection method for reinforcement learning.<n>We evaluate GradAlign across three data regimes: unreliable reward signals, distribution imbalance, and low-utility training corpus.
  arXiv  Detail & Related papers  (2026-02-25T01:54:50Z)
• Architecture-Agnostic Curriculum Learning for Document Understanding: Empirical Evidence from Text-Only and Multimodal [13.329839705160927]
  We investigate whether progressive data scheduling yields consistent efficiency gains across document understanding models.<n>We establish that this schedule reduces wall-clock training time by approximately 33%, commensurate with the reduction from 6.67 to 10.0 effective epoch-reductions of data.
  arXiv  Detail & Related papers  (2026-02-02T10:09:26Z)
• Test-time Offline Reinforcement Learning on Goal-related Experience [50.94457794664909]
  Research in foundation models has shown that performance can be substantially improved through test-time training.<n>We propose a novel self-supervised data selection criterion, which selects transitions from an offline dataset according to their relevance to the current state.<n>Our goal-conditioned test-time training (GC-TTT) algorithm applies this routine in a receding-horizon fashion during evaluation, adapting the policy to the current trajectory as it is being rolled out.
  arXiv  Detail & Related papers  (2025-07-24T21:11:39Z)
• Quality over Quantity: An Effective Large-Scale Data Reduction Strategy Based on Pointwise V-Information [2.133855532092057]
  We propose an effective data reduction strategy based on Pointwise V-Information (PVI)<n>Experiments show that classifier performance is maintained with only a 0.0001% to 0.76% decline in accuracy when 10%-30% of the data is removed.<n>We have adapted the PVI framework, which was previously limited to English datasets, to a variety of Chinese Natural Language Processing (NLP) tasks and base models.
  arXiv  Detail & Related papers  (2025-06-19T06:59:19Z)
• How to Achieve Higher Accuracy with Less Training Points? [2.1834099301440526]
  We propose a technique based on influence functions to determine which training samples should be included in the training set.<n>Our approach demonstrates performance comparable to that of training on the entire dataset while using only 10% of the data.
  arXiv  Detail & Related papers  (2025-04-18T09:38:26Z)
• Optimizing ML Training with Metagradient Descent [69.89631748402377]
  We introduce an algorithm for efficiently calculating metagradients -- gradients through model training -- at scale.<n>We then introduce a "smooth model training" framework that enables effective optimization using metagradients.
  arXiv  Detail & Related papers  (2025-03-17T22:18:24Z)
• What is Your Data Worth to GPT? LLM-Scale Data Valuation with Influence Functions [34.99034454081842]
  Large language models (LLMs) are trained on a vast amount of human-written data, but data providers often remain uncredited. In this work, we focus on influence functions, a popular gradient-based data valuation method, and significantly improve its scalability. We also introduce LogIX, a software package that can transform existing training code into data valuation code with minimal effort.
  arXiv  Detail & Related papers  (2024-05-22T19:39:05Z)
• Monte Carlo Tree Search Boosts Reasoning via Iterative Preference Learning [55.96599486604344]
  We introduce an approach aimed at enhancing the reasoning capabilities of Large Language Models (LLMs) through an iterative preference learning process. We use Monte Carlo Tree Search (MCTS) to iteratively collect preference data, utilizing its look-ahead ability to break down instance-level rewards into more granular step-level signals. The proposed algorithm employs Direct Preference Optimization (DPO) to update the LLM policy using this newly generated step-level preference data.
  arXiv  Detail & Related papers  (2024-05-01T11:10:24Z)
• Online Importance Sampling for Stochastic Gradient Optimization [33.42221341526944]
  We propose a practical algorithm that efficiently computes data importance on-the-fly during training.<n>We also introduce a novel metric based on the derivative of the loss w.r.t. the network output, designed for mini-batch importance sampling.
  arXiv  Detail & Related papers  (2023-11-24T13:21:35Z)
• Efficient Grammatical Error Correction Via Multi-Task Training and Optimized Training Schedule [55.08778142798106]
  We propose auxiliary tasks that exploit the alignment between the original and corrected sentences. We formulate each task as a sequence-to-sequence problem and perform multi-task training. We find that the order of datasets used for training and even individual instances within a dataset may have important effects on the final performance.
  arXiv  Detail & Related papers  (2023-11-20T14:50:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.