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Train on Validation (ToV): Fast data selection with applications to fine-tuning

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
Data selectionInfluence functionInstruction tuningLLM
Abstract:

State-of-the-art machine learning often follows a two-stage process: (i)(i) pre-training on large, general-purpose datasets; (ii)(ii) fine-tuning on task-specific data. In fine-tuning, selecting training examples that closely reflect the target distribution is crucial. However, it is often the case that only a few samples are available from the target distribution. Existing data selection methods treat these target samples as a validation set and estimate the effect of adding or removing a single sample from the training pool by performing inference on the validation set.

We propose a simpler and faster alternative that inverts the usual role of train and validation: we perform inference on the training pool before and after fine-tuning on the validation set. We then select samples whose predictions change the most. Our key insight is that the training samples most affected by fine-tuning on a small validation set tend to be the most beneficial for reducing test loss on the target distribution. Experiments on instruction tuning and named entity recognition tasks show that, in most cases, our method achieves lower test log-loss than state-of-the-art approaches. We support our findings with theoretical analysis.

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This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
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Overview

Overall Novelty Assessment

The paper introduces a Train on Validation (ToV) method that inverts the conventional train-validation relationship: it fine-tunes on a small validation set and selects training samples whose predictions change most. This work resides in the Influence and Importance-Based Scoring leaf, which contains five papers including Importance Resampling, Less, Transferred Shapley Values, and Essence and Dross. The leaf sits within Selection Criteria and Scoring Methods, a moderately populated branch addressing how to assign value to individual training examples. The taxonomy shows this is an active research direction with established gradient-based and game-theoretic approaches.

The taxonomy reveals neighboring leaves focused on Distribution and Diversity-Based Criteria (six papers on alignment and coverage metrics), Model-Aware and Predictive Scoring (four papers leveraging uncertainty and perplexity), and Quality and Noise Filtering (three papers on instance-level filtering). The scope note for Influence and Importance-Based Scoring explicitly excludes distribution matching methods, positioning ToV's prediction-change criterion as distinct from diversity-focused approaches. The broader Selection Criteria and Scoring Methods branch encompasses fourteen papers across four leaves, indicating a well-explored but not overcrowded research area with room for methodological innovation.

Among sixteen candidates examined, no papers clearly refute any of the three contributions. The ToV method itself was compared against four candidates with zero refutations. The efficient approximation via train-validation symmetry examined two candidates, also with no overlapping prior work. The theoretical justification under local convexity reviewed ten candidates without finding substantive precedent. These statistics suggest that within the limited search scope—top-K semantic matches plus citation expansion—the core ideas appear relatively novel, though the analysis does not claim exhaustive coverage of all possible related work in influence-based scoring or data selection more broadly.

Based on the examined candidates and taxonomy position, the work introduces a methodologically distinct approach within an established research direction. The limited search scope (sixteen candidates) means unexamined papers in adjacent leaves or outside the top-K semantic matches could reveal additional connections. The taxonomy structure indicates the paper contributes to a moderately active subfield rather than pioneering an entirely new research area, but the specific inversion of train-validation roles appears underexplored in the sampled literature.

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
16
Contribution Candidate Papers Compared
0
Refutable Paper

Research Landscape Overview

Core task: Data selection for fine-tuning machine learning models. The field has organized itself around several complementary perspectives. Selection Criteria and Scoring Methods explore how to assign value to individual training examples, often through influence functions, importance weights, or diversity metrics, as seen in works like Importance Resampling[1] and Transferred Shapley Values[3]. Selection Algorithms and Optimization focus on the computational machinery for efficiently choosing subsets, including active learning strategies such as Active Fine-tuning[16] and batch selection methods like Utility-Diversity Batch[18]. Task-Specific and Domain-Specific Selection addresses challenges in particular application areas—ranging from code generation (Data-efficient Code Generation[40]) to speech (Speech Data Selection[11])—where domain constraints shape what constitutes a useful training sample. Theoretical Foundations and Empirical Analysis provide the mathematical underpinnings and large-scale experimental insights, while Specialized Constraints and Settings handle scenarios with limited budgets, privacy requirements, or cold-start conditions. Recent work has increasingly emphasized the interplay between scoring mechanisms and downstream task performance. A dense branch of influence-based methods, including Less[2] and Essence and Dross[5], seeks to identify which examples most shape model behavior, often leveraging gradient information or proxy models like SmallToLarge[17]. Train on Validation[0] sits within this influence and importance-based scoring cluster, proposing a distinctive approach that uses validation set performance as a direct signal for sample selection. This contrasts with neighbors like Transferred Shapley Values[3], which rely on cooperative game theory to allocate credit, and Importance Resampling[1], which adjusts sampling probabilities based on estimated example utility. Across these lines, a central tension persists: balancing the computational cost of sophisticated scoring against the practical gains in fine-tuning efficiency, especially as models and datasets scale.

Claimed Contributions

Train on Validation (ToV) data selection method

The authors introduce a data selection method that reverses the typical train-validation relationship by fine-tuning on a small validation set and measuring prediction changes on training samples, rather than evaluating validation loss changes from training on individual samples. This approach avoids computing per-example gradients or Hessian-vector products.

4 retrieved papers
Efficient approximation via train-validation symmetry

The method exploits a symmetry property showing that the decrease in validation loss from training on a sample x mirrors the decrease in loss on x from training on validation data. This enables efficient score computation requiring only two forward passes over the training pool and one epoch on validation, instead of N validation evaluations.

2 retrieved papers
Theoretical justification under local convexity

The authors provide formal mathematical analysis showing that their ToV scores approximate ideal influence-based scores under local convexity conditions, and prove convergence to classical influence functions for M-estimators in the limit of many training epochs.

10 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Train on Validation (ToV) data selection method

The authors introduce a data selection method that reverses the typical train-validation relationship by fine-tuning on a small validation set and measuring prediction changes on training samples, rather than evaluating validation loss changes from training on individual samples. This approach avoids computing per-example gradients or Hessian-vector products.

Contribution

Efficient approximation via train-validation symmetry

The method exploits a symmetry property showing that the decrease in validation loss from training on a sample x mirrors the decrease in loss on x from training on validation data. This enables efficient score computation requiring only two forward passes over the training pool and one epoch on validation, instead of N validation evaluations.

Contribution

Theoretical justification under local convexity

The authors provide formal mathematical analysis showing that their ToV scores approximate ideal influence-based scores under local convexity conditions, and prove convergence to classical influence functions for M-estimators in the limit of many training epochs.

Table of Contents