From Detrimental to Beneficial: Dynamic Influence-based Valuation and Editing

• URL: http://arxiv.org/abs/2608.22522v1
• Date: Sun, 23 Aug 2026 17:46:18 GMT
• Title: From Detrimental to Beneficial: Dynamic Influence-based Valuation and Editing
• Abstract summary: We present Dynamic Influence-based Valuation and Editing (DIVE), a novel and efficient framework that dynamically estimates sample values at the batch level.<n>Rather than altering the raw data, DIVE operates at the optimization level by strategically reversing the gradient directions of harmful samples during training.
• Score: 5.184176329801992
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Data valuation is a cornerstone of data-centric learning, where prior efforts primarily focus on designing algorithms to classify training samples as either beneficial or detrimental for the learning task. However, leveraging these valuation estimates for subsequent data intervention remains underexplored; conventional approaches typically discard or downweight harmful samples, thereby underutilizing available data resources. In this paper, we present Dynamic Influence-based Valuation and Editing (DIVE), a novel and efficient framework that dynamically estimates sample values at the batch level and transforms detrimental data into beneficial contributions. Rather than altering the raw data, DIVE operates at the optimization level by strategically reversing the gradient directions of harmful samples during training, ensuring seamless integration with standard learning procedures with minimal overhead. Extensive empirical evaluations demonstrate that DIVE consistently improves classification performance, maximizes data efficiency, stabilizes optimization, and effectively generalizes to large language model fine-tuning.

Related papers

• IADD-TR: Intervention-Aware Dynamics Decoupling with Targeted Regularization for Model-Based Reinforcement Learning [57.16513298507272]
  We propose IADD-TR, a unified framework combining Intervention-Aware Dynamics Decoupling (IADD) and Targeted Regularization (TR)<n>IADD factorizes transitions into an action-intervention stage and an action-free natural evolution stage, using a zero-action anchor to resolve the non-uniqueness of this two-stage factorization for robust generalization.<n>For policy learning, we derive TR from the efficient influence function of a replay-state policy-gradient functional.
  arXiv  Detail & Related papers  (2026-08-11T08:20:02Z)
• A novel Information-Driven Strategy for Optimal Regression Assessment [3.602068950014566]
  In Machine Learning (ML), a regression algorithm aims to minimize a loss function based on data.<n>No data-driven assessment method can ensure the achievability of global optimality.<n>This work introduces the Information Teacher, a novel data-driven framework for evaluating regression algorithms.
  arXiv  Detail & Related papers  (2025-10-16T02:01:32Z)
• Layer-Aware Influence for Online Data Valuation Estimation [32.294500546369136]
  Data-centric learning emphasizes curating high-quality training samples to boost performance.<n>A central problem is to estimate the influence of training sample efficiently.<n>We develop a layer-aware online estimator that requires only loss-to-output gradients.
  arXiv  Detail & Related papers  (2025-10-14T15:34:22Z)
• Improving Model Classification by Optimizing the Training Dataset [3.987352341101438]
  Coresets offer a principled approach to data reduction, enabling efficient learning on large datasets.<n>We present a systematic framework for tuning the coreset generation process to enhance downstream classification quality.
  arXiv  Detail & Related papers  (2025-07-22T16:10:11Z)
• Sharpe Ratio-Guided Active Learning for Preference Optimization in RLHF [67.48004037550064]
  We propose an active learning approach to efficiently select prompt and preference pairs.<n>Our method evaluates the gradients of all potential preference annotations to assess their impact on model updates.<n> Experimental results demonstrate that our method outperforms the baseline by up to 5% in win rates against the chosen completion.
  arXiv  Detail & Related papers  (2025-03-28T04:22:53Z)
• Dynamic Loss-Based Sample Reweighting for Improved Large Language Model Pretraining [55.262510814326035]
  Existing reweighting strategies primarily focus on group-level data importance.<n>We introduce novel algorithms for dynamic, instance-level data reweighting.<n>Our framework allows us to devise reweighting strategies deprioritizing redundant or uninformative data.
  arXiv  Detail & Related papers  (2025-02-10T17:57:15Z)
• Distilled Datamodel with Reverse Gradient Matching [74.75248610868685]
  We introduce an efficient framework for assessing data impact, comprising offline training and online evaluation stages. Our proposed method achieves comparable model behavior evaluation while significantly speeding up the process compared to the direct retraining method.
  arXiv  Detail & Related papers  (2024-04-22T09:16:14Z)
• Direct Acquisition Optimization for Low-Budget Active Learning [15.355195433709717]
  Active Learning (AL) has gained prominence in integrating data-intensive machine learning (ML) models into domains with limited labeled data. In this paper, we first empirically observe the performance degradation of existing AL algorithms in the low-budget settings. We then introduce Direct Acquisition Optimization (DAO), a novel AL algorithm that optimize sample selections based on expected true loss reduction.
  arXiv  Detail & Related papers  (2024-02-08T20:36:21Z)
• Temporal Output Discrepancy for Loss Estimation-based Active Learning [65.93767110342502]
  We present a novel deep active learning approach that queries the oracle for data annotation when the unlabeled sample is believed to incorporate high loss. Our approach achieves superior performances than the state-of-the-art active learning methods on image classification and semantic segmentation tasks.
  arXiv  Detail & Related papers  (2022-12-20T19:29:37Z)
• Domain Adaptation with Adversarial Training on Penultimate Activations [82.9977759320565]
  Enhancing model prediction confidence on unlabeled target data is an important objective in Unsupervised Domain Adaptation (UDA) We show that this strategy is more efficient and better correlated with the objective of boosting prediction confidence than adversarial training on input images or intermediate features.
  arXiv  Detail & Related papers  (2022-08-26T19:50:46Z)

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.