Data Agent: Learning to Select Data via End-to-End Dynamic Optimization

• URL: http://arxiv.org/abs/2603.07433v1
• Date: Sun, 08 Mar 2026 03:10:39 GMT
• Title: Data Agent: Learning to Select Data via End-to-End Dynamic Optimization
• Abstract summary: Data Agent formulates data selection as a training-aware sequential decision-making problem.<n>Experiments demonstrate that Data Agent consistently accelerates training while preserving or improving performance.<n>Its dataset-agnostic formulation and modular reward make it plug-and-play across tasks and scenarios.
• Score: 37.1771265765151
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Dynamic Data selection aims to accelerate training by prioritizing informative samples during online training. However, existing methods typically rely on task-specific handcrafted metrics or static/snapshot-based criteria to estimate sample importance, limiting scalability across learning paradigms and making it difficult to capture the evolving utility of data throughout training. To address this challenge, we propose Data Agent, an end-to-end dynamic data selection framework that formulates data selection as a training-aware sequential decision-making problem. The agent learns a sample-wise selection policy that co-evolves with model optimization, guided by a composite reward that integrates loss-based difficulty and confidence-based uncertainty signals. The reward signals capture complementary objectives of optimization impact and information gain, together with a tuning-free adaptive weighting mechanism that balances these signals over training. Extensive experiments across a wide range of datasets and architectures demonstrate that Data Agent consistently accelerates training while preserving or improving performance, e.g., reducing costs by over 50\% on ImageNet-1k and MMLU with lossless performance. Moreover, its dataset-agnostic formulation and modular reward make it plug-and-play across tasks and scenarios, e.g., robustness to noisy datasets, highlighting its potential in real-world scenarios.

Related papers

• Holistic Data Scheduler for LLM Pre-training via Multi-Objective Reinforcement Learning [7.431998098521794]
  We introduce the Holistic Data Scheduler (HDS), a novel online data mixing framework.<n>HDS formulates the data scheduling challenge as a reinforcement learning problem in a continuous control space.<n>It integrates a data-driven reward for quality, a loss-driven reward capturing inter-domain influence, and a model-driven reward based on weight norms.
  arXiv  Detail & Related papers  (2026-06-23T04:32:46Z)
• Beyond What to Select: A Plug-and-play Oscillatory Data-Volume Scheduling for Efficient Model Training [36.289092974454796]
  We show that selected-data training induces an implicit regularization effect by the instantaneous selection ratio.<n>This reveals a key trade-off: lower amplify selection-induced regularization, whereas higher ratios preserve data coverage.<n> PODS is compatible with existing static and dynamic selection methods and broadly applicable across training paradigms.
  arXiv  Detail & Related papers  (2026-05-14T12:37:11Z)
• Adaptive Data Dropout: Towards Self-Regulated Learning in Deep Neural Networks [10.269961654607108]
  Deep neural networks are typically trained by uniformly sampling large datasets across epochs.<n>Recent work shows that progressively reducing the amount of training data can improve efficiency and generalization.<n>We propose Adaptive Data Dropout, a framework that dynamically adjusts the subset of training data based on performance feedback.
  arXiv  Detail & Related papers  (2026-04-14T16:41:33Z)
• ZPD Detector: Data Selection via Capability-Difficulty Alignment for Large Language Models [9.072905469784951]
  We propose ZPD Detector, a data selection framework that adopts a bidirectional perspective between models and data.<n>ZPD Detector integrates difficulty calibration, model capability estimation based on Item Response Theory (IRTy), and a capability-difficult matching score to dynamically identify the most informative samples.
  arXiv  Detail & Related papers  (2026-01-16T04:23:10Z)
• RL-Selector: Reinforcement Learning-Guided Data Selection via Redundancy Assessment [10.284993431741377]
  We introduce the concept of epsilon-sample cover, which quantifies sample redundancy based on inter-sample relationships.<n>We reformulate data selection as a reinforcement learning process and propose RL-Selector.<n>Our method consistently outperforms existing state-of-the-art baselines.
  arXiv  Detail & Related papers  (2025-06-26T06:28:56Z)
• When Dynamic Data Selection Meets Data Augmentation [10.217776379089093]
  We propose a novel online data training framework that unifies dynamic data selection and augmentation.<n>Our method estimates each sample's joint distribution of local density and multimodal semantic consistency, allowing for the targeted selection of augmentation-suitable samples.<n>Our approach enhances noise resistance and improves model robustness, reinforcing its practical utility in real-world scenarios.
  arXiv  Detail & Related papers  (2025-05-02T11:38:48Z)
• FedAWA: Adaptive Optimization of Aggregation Weights in Federated Learning Using Client Vectors [50.131271229165165]
  Federated Learning (FL) has emerged as a promising framework for distributed machine learning.<n>Data heterogeneity resulting from differences across user behaviors, preferences, and device characteristics poses a significant challenge for federated learning.<n>We propose Adaptive Weight Aggregation (FedAWA), a novel method that adaptively adjusts aggregation weights based on client vectors during the learning process.
  arXiv  Detail & Related papers  (2025-03-20T04:49:40Z)
• 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)
• Capturing the Temporal Dependence of Training Data Influence [100.91355498124527]
  We formalize the concept of trajectory-specific leave-one-out influence, which quantifies the impact of removing a data point during training.<n>We propose data value embedding, a novel technique enabling efficient approximation of trajectory-specific LOO.<n>As data value embedding captures training data ordering, it offers valuable insights into model training dynamics.
  arXiv  Detail & Related papers  (2024-12-12T18:28:55Z)
• A CLIP-Powered Framework for Robust and Generalizable Data Selection [51.46695086779598]
  Real-world datasets often contain redundant and noisy data, imposing a negative impact on training efficiency and model performance.<n>Data selection has shown promise in identifying the most representative samples from the entire dataset.<n>We propose a novel CLIP-powered data selection framework that leverages multimodal information for more robust and generalizable sample selection.
  arXiv  Detail & Related papers  (2024-10-15T03:00:58Z)
• LESS: Selecting Influential Data for Targeted Instruction Tuning [64.78894228923619]
  We propose LESS, an efficient algorithm to estimate data influences and perform Low-rank gradiEnt Similarity Search for instruction data selection. We show that training on a LESS-selected 5% of the data can often outperform training on the full dataset across diverse downstream tasks. Our method goes beyond surface form cues to identify data that the necessary reasoning skills for the intended downstream application.
  arXiv  Detail & Related papers  (2024-02-06T19:18:04Z)
• Data-Driven Offline Decision-Making via Invariant Representation Learning [97.49309949598505]
  offline data-driven decision-making involves synthesizing optimized decisions with no active interaction. A key challenge is distributional shift: when we optimize with respect to the input into a model trained from offline data, it is easy to produce an out-of-distribution (OOD) input that appears erroneously good. In this paper, we formulate offline data-driven decision-making as domain adaptation, where the goal is to make accurate predictions for the value of optimized decisions.
  arXiv  Detail & Related papers  (2022-11-21T11:01:37Z)

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.