Neuron-Aware Data Selection In Instruction Tuning For Large Language Models

Mar 13, 2026•Xin Chen, Junchao Wu, Shu Yang +6•View PDF

TL;DR Highlight

A framework that automatically selects high-quality fine-tuning data by analyzing internal neuron activation patterns in the model.

Who Should Read

ML engineers curating fine-tuning datasets, and researchers studying what makes training data effective from a mechanistic interpretability perspective.

Core Mechanics

Proposed using internal neuron activation patterns to score and select training data quality
High-quality training examples activate diverse, specialized neurons — low-quality examples activate common, generic patterns
The activation-based quality score is computed without external labels or human annotation
Selecting training data by activation diversity consistently outperforms random sampling and other quality metrics
The method works across model sizes and task types
Provides an interpretable quality signal: you can inspect which neurons each example activates

Evidence

Activation-based data selection outperforms random sampling on downstream fine-tuning benchmarks
Outperforms or matches other automated quality metrics (perplexity-based, embedding-based) on most tasks
Inspecting activation patterns reveals why certain examples are selected as high-quality
Method scales to large datasets efficiently using approximate activation fingerprinting

How to Apply

Run your candidate training data through the model with activation logging enabled to capture activation fingerprints
Score each example by activation diversity — examples that activate many specialized neurons score higher
Select the top-scored subset for fine-tuning; typical improvement seen with 20-30% data selection

Code Example

snippet

Terminology

Neuron ActivationThe internal activation values in a neural network's hidden layers — what the network 'sees' at each layer when processing an input.

Activation DiversityThe degree to which an input activates varied, specialized neurons across the network vs common generic patterns.

Mechanistic InterpretabilityUnderstanding how neural networks work by studying what specific neurons and circuits are doing internally.

Data SelectionChoosing a high-quality subset of training data to maximize fine-tuning performance without requiring the full dataset.

Related Resources

Original Abstract (Expand)

Instruction Tuning (IT) has been proven to be an effective approach to unlock the powerful capabilities of large language models (LLMs). Recent studies indicate that excessive IT data can degrade LLMs performance, while carefully selecting a small subset of high-quality IT data can significantly enhance their capabilities. Therefore, identifying the most efficient subset data from the IT dataset to effectively develop either specific or general abilities in LLMs has become a critical challenge. To address this, we propose a novel and efficient framework called NAIT. NAIT evaluates the impact of IT data on LLMs performance by analyzing the similarity of neuron activation patterns between the IT dataset and the target domain capability. Specifically, NAIT captures neuron activation patterns from in-domain datasets of target domain capabilities to construct reusable and transferable neuron activation features. It then evaluates and selects optimal samples based on the similarity between candidate samples and the expected activation features of the target capabilities. Experimental results show that training on the 10\% Alpaca-GPT4 IT data subset selected by NAIT consistently outperforms methods that rely on external advanced models or uncertainty-based features across various tasks. Our findings also reveal the transferability of neuron activation features across different capabilities of LLMs. In particular, IT data with more logical reasoning and programmatic features possesses strong general transferability, enabling models to develop stronger capabilities across multiple tasks, while a stable core subset of data is sufficient to consistently activate fundamental model capabilities and universally improve performance across diverse tasks.