SLAP: Stratified Loss-based Pruning for On-Policy Data-Efficient Instruction Tuning

• URL: http://arxiv.org/abs/2605.23969v1
• Date: Wed, 13 May 2026 06:36:24 GMT
• Title: SLAP: Stratified Loss-based Pruning for On-Policy Data-Efficient Instruction Tuning
• Abstract summary: We propose textbfSLAP, a novel batch-aware data selection framework.<n>SLAP evaluates the learnability of entire batch compositions rather than individual.<n>SLAP achieves superior performance with 20-40% less training data compared to full dataset training.
• Score: 14.87050258251449
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Instruction tuning has optimized the specialized capabilities of large language models (LLMs), but it often requires extensive datasets and prolonged training times. The challenge lies in developing specific capabilities by identifying useful data and efficiently fine-tuning. High-quality and diverse pruned data can help models achieve lossless performance at a lower cost. In this paper, we propose \textbf{SLAP}, a novel batch-aware data selection framework that evaluates the learnability of entire batch compositions rather than individual. SLAP ensures comprehensive data distribution coverage through distribution-aware stratified sampling while maximizing intra-batch diversity through relative distance optimization. By leveraging Hessian-approximated gradient information for dynamic batch selection, SLAP significantly outperforms existing state-of-the-art methods across multiple model architectures (LLaMA, ChatGLM) and diverse downstream tasks including multi-turn dialogue, multilingual translation, and question answering. Most notably, SLAP achieves superior performance with 20-40\% less training data compared to full dataset training, substantially reducing computational costs while maintaining or improving model capabilities. These results establish SLAP as a powerful approach for efficient and effective instruction tuning of large language models.

Related papers

• Learning Multi-Indicator Weights for Data Selection: A Joint Task-Model Adaptation Framework with Efficient Proxies [50.39041754816285]
  We propose a framework for learning multi-validation weights that jointly adapts data selection to both the downstream task and the specific model.<n>Our approach achieves performance comparable to or exceeding full-dataset tuning while using only 30% of the training samples on GSM8K.
  arXiv  Detail & Related papers  (2026-05-10T17:30:16Z)
• Learn More, Forget Less: A Gradient-Aware Data Selection Approach for LLM [51.21051698747157]
  We propose a self-adaptive gradient-aware data selection approach (GrADS) for supervised fine-tuning of large language models (LLMs)<n>Specifically, we design self-guided criteria that leverage the magnitude and statistical distribution of gradients to prioritize examples that contribute the most to the model's learning process.<n>Through extensive experimentation with various LLMs across diverse domains such as medicine, law, and finance, GrADS has demonstrated significant efficiency and cost-effectiveness.
  arXiv  Detail & Related papers  (2025-11-07T08:34:50Z)
• Utility-Diversity Aware Online Batch Selection for LLM Supervised Fine-tuning [49.04912820721943]
  Supervised fine-tuning (SFT) is computationally expensive and sometimes suffers from overfitting or bias amplification.<n>This work studies the online batch selection family that dynamically scores and filters samples during the training process.<n>We develop textbfUDS (Utility-Diversity Sampling), a framework for efficient online batch selection in SFT.
  arXiv  Detail & Related papers  (2025-10-19T15:32:01Z)
• InfiAlign: A Scalable and Sample-Efficient Framework for Aligning LLMs to Enhance Reasoning Capabilities [27.09178257629886]
  InfiAlign is a scalable and sample-efficient post-training framework for large language models (LLMs)<n>At the core of InfiAlign is a robust data selection pipeline that automatically curates high-quality alignment data from open-source reasoning.<n>Our results highlight the effectiveness of combining principled data selection with full-stage post-training.
  arXiv  Detail & Related papers  (2025-08-07T15:34:06Z)
• SPaRFT: Self-Paced Reinforcement Fine-Tuning for Large Language Models [51.74498855100541]
  Large language models (LLMs) have shown strong reasoning capabilities when fine-tuned with reinforcement learning (RL)<n>We propose textbfSPaRFT, a self-paced learning framework that enables efficient learning based on the capability of the model being trained.
  arXiv  Detail & Related papers  (2025-08-07T03:50:48Z)
• LASER: Stratified Selective Sampling for Instruction Tuning with Dedicated Scoring Strategy [5.130520244165049]
  We show that data selection can be both -- efficient and universal -- by using a multi-step pipeline.<n>We use task-based categorization to control the composition of our final data.<n>This integrated strategy enables high-performance fine-tuning with minimal overhead.
  arXiv  Detail & Related papers  (2025-05-28T09:22:25Z)
• 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)
• RPLKG: Robust Prompt Learning with Knowledge Graph [14.531071492983767]
  multimodal pre-trained models like CLIP have significantly boosted performance in various experiments.<n>Existing methods often lack interpretability and impose high computational costs.<n>We propose Robust Prompt Learning with Knowledge Graph (RPLKG) to curate diverse, interpretable prompt sets automatically.
  arXiv  Detail & Related papers  (2023-04-21T08:22:58Z)

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.