EvoSelect: Data-Efficient LLM Evolution for Targeted Task Adaptation

• URL: http://arxiv.org/abs/2604.26170v1
• Date: Tue, 28 Apr 2026 23:26:16 GMT
• Title: EvoSelect: Data-Efficient LLM Evolution for Targeted Task Adaptation
• Abstract summary: Adapting large language models (LLMs) to a targeted task efficiently and effectively remains a fundamental challenge.<n>One straightforward approach is through an iterative generation-training loop, where candidate data are synthesized through an external generator.<n>We introduce a refined paradigm, namely an iterative generation-selection-training loop, which incorporates a selection step prior to model updates.
• Score: 79.71802168256542
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Adapting large language models (LLMs) to a targeted task efficiently and effectively remains a fundamental challenge. Such adaptation often requires iteratively improving the model toward a targeted task, yet collecting high-quality human-labeled data to support this process is costly and difficult to scale. As a result, synthetic data generation has emerged as a flexible and scalable alternative. One straightforward approach is through an iterative generation-training loop, where candidate data are synthesized through an external generator, the model is updated using these data and the process is repeated over iterations. However, generated samples can be noisy, highly redundant, or even misaligned with the targeted task distribution. Training indiscriminately on such data can dilute useful learning signals and even degrade model performance. To address this, we introduce a refined paradigm, namely an iterative generation-selection-training loop, which incorporates a selection step prior to model updates. Building on this paradigm, we propose EvoSelect, a data-efficient framework to evolve LLM effectively. Given candidate samples produced by the data generator, EvoSelect selects training data by jointly modeling targeted task alignment and diversity. We estimate task relevance through optimal transport with proxy gradient representations, which quantifies how well candidate samples align with the targeted task distribution. To mitigate redundancy, we incorporate a diversification mechanism that promotes coverage of complementary training samples. By interleaving alignment and diversification, EvoSelect enables progressive LLM evolution toward targeted tasks. Extensive experiments on various benchmarks demonstrate that with either weak or strong data generators, EvoSelect consistently improves adaptation efficacy over existing data selection methods.

Related papers

• Data Selection Through Iterative Self-Filtering for Vision-Language Settings [2.9868893900614406]
  We propose a novel, bootstrapped method in which a CLIP model is trained on an evolving, self-selected dataset.<n>Our proposed Self-Filtering method iterates between training the model and selecting a subsequently improved data mixture.
  arXiv  Detail & Related papers  (2026-06-22T17:11:15Z)
• 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)
• DOSE: Data Selection for Multi-Modal LLMs via Off-the-Shelf Models [43.094426671099]
  We study whether off-the-shelf pretrained models that have never seen the target data can be used to select training samples for larger and stronger multimodal models without any task-specific training.<n>We build a joint quality-alignment distribution and apply adaptive weighted sampling to select informative samples while maintaining long-tail diversity.<n>This approach enhances data diversity, enabling models trained on DOSE-filtered data to match or surpass those trained on the full dataset on standard VQA and math benchmarks.
  arXiv  Detail & Related papers  (2026-04-18T12:41:06Z)
• Add-One-In: Incremental Sample Selection for Large Language Models via a Choice-Based Greedy Paradigm [50.492124556982674]
  This paper introduces a novel choice-based sample selection framework.<n>It shifts the focus from evaluating individual sample quality to comparing the contribution value of different samples.<n>We validate our approach on a larger medical dataset, highlighting its practical applicability in real-world applications.
  arXiv  Detail & Related papers  (2025-03-04T07:32:41Z)
• Forewarned is Forearmed: Leveraging LLMs for Data Synthesis through Failure-Inducing Exploration [90.41908331897639]
  Large language models (LLMs) have significantly benefited from training on diverse, high-quality task-specific data. We present a novel approach, ReverseGen, designed to automatically generate effective training samples.
  arXiv  Detail & Related papers  (2024-10-22T06:43:28Z)
• Adapt-$\infty$: Scalable Continual Multimodal Instruction Tuning via Dynamic Data Selection [89.42023974249122]
  Adapt-$infty$ is a new multi-way and adaptive data selection approach for lifelong instruction tuning.<n>We construct pseudo-skill clusters by grouping gradient-based sample vectors.<n>We select the best-performing data selector for each skill cluster from a pool of selector experts.<n>This data selector samples a subset of the most important samples from each skill cluster for training.
  arXiv  Detail & Related papers  (2024-10-14T15:48:09Z)
• Progressively Label Enhancement for Large Language Model Alignment [42.01694160556464]
  Large Language Models (LLM) alignment aims to prevent models from producing content that misaligns with human expectations. We propose PLE, a framework that dynamically adjusts the model's training process based on the evolving quality of the generated data.
  arXiv  Detail & Related papers  (2024-08-05T16:21:17Z)
• 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)
• Self-Evolved Diverse Data Sampling for Efficient Instruction Tuning [47.02160072880698]
  We introduce a self-evolving mechanism that allows the model itself to actively sample subsets that are equally or even more effective. The key to our data sampling technique lies in the enhancement of diversity in the chosen subsets. Extensive experiments across three datasets and benchmarks demonstrate the effectiveness of DiverseEvol.
  arXiv  Detail & Related papers  (2023-11-14T14:10:40Z)
• Self-Supervised Dataset Distillation for Transfer Learning [77.4714995131992]
  We propose a novel problem of distilling an unlabeled dataset into a set of small synthetic samples for efficient self-supervised learning (SSL) We first prove that a gradient of synthetic samples with respect to a SSL objective in naive bilevel optimization is textitbiased due to randomness originating from data augmentations or masking. We empirically validate the effectiveness of our method on various applications involving transfer learning.
  arXiv  Detail & Related papers  (2023-10-10T10:48:52Z)

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.