arxiv: 2605.09665 · v1 · submitted 2026-05-10 · 💻 cs.LG · cs.AI· cs.CL

Recognition: 2 theorem links

· Lean Theorem

Learning Multi-Indicator Weights for Data Selection: A Joint Task-Model Adaptation Framework with Efficient Proxies

Jingze Song , Zihao Chen , Wenqing Chen , Zibin Zheng

Authors on Pith no claims yet

Pith reviewed 2026-05-12 04:44 UTC · model grok-4.3

classification 💻 cs.LG cs.AIcs.CL

keywords data selectioninstruction tuninglarge language modelsmulti-indicator weightstask-model adaptationin-context learningefficient proxiesGSM8K

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The pith

A joint adaptation framework learns weights for data indicators using tiny-set ICL proxies to match full fine-tuning with 30 percent samples.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper proposes a method to learn weights for multiple data quality indicators so that data selection adapts to both a specific downstream task and the particular strengths of the target model. It finds good weight settings by collecting in-context learning signals on small validation sets that serve as low-cost proxies for full fine-tuning results. This avoids expensive trial fine-tunings while still identifying high-performing data subsets. Across benchmarks and models such as Mistral, Qwen, and Llama, the approach reaches or exceeds the accuracy of training on the complete dataset when using only 30 percent of the samples on GSM8K. The work additionally identifies a trade-off between semantic diversity and logical complexity in the data chosen for reasoning tasks.

Core claim

The framework learns optimal weight configurations for multi-indicator data selection by treating in-context learning signals on compact tiny-validation sets as efficient performance proxies. These proxies enable joint adaptation of the selection process to the demands of the target task and the existing capabilities of the model without requiring full-scale fine-tuning trials. On the GSM8K benchmark the resulting subsets, containing only 30 percent of the training samples, produce accuracy comparable to or better than full-dataset tuning across several large language model families.

What carries the argument

The joint task-model adaptation framework that learns multi-indicator weights via in-context learning signals on tiny-validation sets as performance proxies.

Load-bearing premise

Signals from in-context learning on small validation sets reliably predict the performance that full fine-tuning would achieve on the selected data.

What would settle it

Actual full fine-tuning on the 30-percent subsets chosen by the learned weights yields substantially lower performance than full-dataset tuning on GSM8K, or the proxy rankings fail to match the ordering obtained from real fine-tuning outcomes.

Figures

Figures reproduced from arXiv: 2605.09665 by Jingze Song, Wenqing Chen, Zibin Zheng, Zihao Chen.

**Figure 1.** Figure 1: Overview of the Task-Model Adaptation with efficient Proxies (TMAP) data selection pipeline. The framework scores candidate [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗

**Figure 2.** Figure 2: Column chart visualization of learned heuristic weights [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗

read the original abstract

Data selection is a key component of efficient instruction tuning for large language models, as recent work has shown that data quality often matters more than data quantity. Accordingly, prior studies have introduced various multi-dimensional heuristics to evaluate and filter instruction data. However, most existing methods rely on static task-agnostic and model-agnostic weighting schemes, which overlook the varying requirements of specific downstream tasks and the differing pre-existing capabilities of models. In this paper, we propose a framework for learning multi-indicator weights that jointly adapts data selection to both the downstream task and the specific model. Our method identifies optimal weight configurations without full-scale fine-tuning by utilizing in-context learning (ICL) signals on compact tiny-validation sets. These signals serve as efficient performance proxies that ensure high-fidelity evaluation at minimal computational cost. Experiments across multiple benchmarks and model families, including Mistral, Qwen, and Llama, show that the approach achieves performance comparable to or exceeding full-dataset tuning while using only 30\% of the training samples on GSM8K. Furthermore, our analysis reveals a trade-off between semantic diversity and logical complexity in reasoning tasks, highlighting the necessity of joint task-model adaptation.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

The paper's idea of learning adaptive weights for data selection via ICL proxies on tiny sets is practical in principle, but the abstract gives no evidence that those proxies actually predict fine-tuning results.

read the letter

This paper's main claim is that you can learn weights across multiple data indicators to pick a 30% subset of instruction data, adapt that selection to both the downstream task and the base model, and still match or beat full-dataset fine-tuning on GSM8K. They avoid expensive full runs by treating in-context learning accuracy on compact validation sets as a stand-in signal for what the selected data would do after actual parameter updates.

Referee Report

2 major / 1 minor

Summary. The paper proposes a joint task-model adaptation framework for learning multi-indicator weights in data selection for LLM instruction tuning. It uses in-context learning (ICL) signals collected on compact tiny-validation sets as efficient proxies to determine optimal weights without requiring full-scale fine-tuning. Experiments across benchmarks and model families (Mistral, Qwen, Llama) are claimed to show performance comparable to or exceeding full-dataset tuning while using only 30% of training samples on GSM8K, with additional analysis of trade-offs between semantic diversity and logical complexity.

Significance. If the central empirical claims hold after proper validation, the work would offer a practical advance in efficient data selection for instruction tuning by reducing data and compute requirements while preserving performance. The emphasis on joint adaptation to both task and model, along with the use of low-cost proxies, addresses limitations in static weighting schemes from prior work.

major comments (2)

[Abstract] Abstract: The headline claim that the method 'achieves performance comparable to or exceeding full-dataset tuning while using only 30% of the training samples on GSM8K' is presented without any reported baselines, statistical tests, ablation controls, or details on tiny-validation set construction. This information is required to assess whether the data support the efficiency claim.
[Abstract] Abstract (and method description): The framework rests on the assumption that ICL signals on tiny-validation sets serve as high-fidelity proxies for downstream accuracy after actual fine-tuning on the selected 30% subset. No correlation study, ablation, or direct comparison between proxy-ranked subsets and their post-fine-tuning scores is described, leaving the central efficiency argument without an empirical anchor.

minor comments (1)

[Abstract] Abstract: The statement that 'our analysis reveals a trade-off between semantic diversity and logical complexity' is mentioned but not located to a specific section or supported with quantitative results or figures.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments regarding the presentation of our empirical claims in the abstract. We address each major comment below and have updated the manuscript accordingly to provide better support for the efficiency claims.

read point-by-point responses

Referee: [Abstract] Abstract: The headline claim that the method 'achieves performance comparable to or exceeding full-dataset tuning while using only 30% of the training samples on GSM8K' is presented without any reported baselines, statistical tests, ablation controls, or details on tiny-validation set construction. This information is required to assess whether the data support the efficiency claim.

Authors: We agree that the abstract would benefit from additional context to support the headline claim. The full manuscript provides extensive baselines (full-dataset tuning, random sampling, and competing data selection methods), ablation studies on the indicators, and statistical analysis (results averaged over multiple runs with significance testing) in Section 4. Details on the construction of the tiny-validation sets are given in Section 3.2. In the revised abstract, we have added a clause referencing these elements to make the claim more self-contained while respecting length limits: 'Experiments across benchmarks and model families, including Mistral, Qwen, and Llama, show that the approach achieves performance comparable to or exceeding full-dataset tuning while using only 30% of the training samples on GSM8K, with supporting baselines and analysis in the experiments.' This revision makes the abstract more informative without altering the core message. revision: yes
Referee: [Abstract] Abstract (and method description): The framework rests on the assumption that ICL signals on tiny-validation sets serve as high-fidelity proxies for downstream accuracy after actual fine-tuning on the selected 30% subset. No correlation study, ablation, or direct comparison between proxy-ranked subsets and their post-fine-tuning scores is described, leaving the central efficiency argument without an empirical anchor.

Authors: The assumption that ICL signals on tiny-validation sets act as reliable proxies is central to our framework's efficiency. While the manuscript demonstrates this through the strong end-to-end performance of the selected subsets, we acknowledge that a direct correlation or ablation comparing proxy-based selection to actual fine-tuning-based selection is not explicitly presented. To address this, we have added a new analysis in the revised manuscript (Section 4.5 and Appendix C) that includes a correlation study between the ICL proxy scores and the actual fine-tuning accuracies on the selected data subsets, along with an ablation showing the performance gap between proxy-selected and oracle-selected subsets. This provides the requested empirical anchor for the proxy fidelity. revision: yes

Circularity Check

0 steps flagged

No significant circularity; empirical proxy-based method with external validation

full rationale

The paper describes an empirical procedure for learning multi-indicator weights via ICL signals on tiny-validation sets as performance proxies, followed by experimental validation on benchmarks like GSM8K across model families. No derivation chain, equations, or self-referential fitting is present in the provided text that reduces predictions to inputs by construction. The proxy assumption is an external empirical hypothesis tested via experiments rather than a self-definitional or fitted-input loop. This matches the default expectation for non-circular empirical ML papers; the central efficiency claim rests on reported results, not tautological reduction.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract alone supplies no concrete information on free parameters, background axioms, or newly postulated entities; the method is described only at the level of high-level components.

pith-pipeline@v0.9.0 · 5520 in / 1071 out tokens · 43072 ms · 2026-05-12T04:44:53.895833+00:00 · methodology

discussion (0)

Reference graph

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A Performance comparison between SFT and ICT Shots GPQA ICL GPQA SFT MUSR ICL MUSR SFT 6432.47 32.51 42.59 42.72 3233.05 32.89 44.05 42.99 1631.88 32.21 43.52 42.86 832.38 32.55 43.39 42.59 Table 8: Performance comparison across different shots on GPQA and MUSR. Results are accuracy (%). The best score is highlighted in bold. We use the same quantity of d...

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