Accelerate Scaling of LLM Alignment via Quantifying the Coverage and Depth of Instruction Set

2025-09-08

AI Summary

Instruction tuning faces diminishing returns as instruction set size increases, with performance plateauing despite scaling. Method: This paper identifies instruction depth and semantic coverage as two critical dimensions governing alignment quality, and introduces the first interpretable, scalable quantitative metrics for both. Leveraging instruction distribution analysis, we design a joint optimization algorithm that simultaneously maximizes semantic coverage and depth, integrated with development-set loss modeling for efficient data selection. Contribution/Results: Our approach enables “accelerated scaling”—sustained performance improvement as the instruction pool grows. It significantly outperforms existing instruction filtering methods across multiple benchmarks, achieving faster convergence with fewer training examples while explaining over 70% of the development-set loss variation. This establishes a novel paradigm for efficient large-model alignment.

Technology Category

• Search and Optimization: Learning to Search
• Natural Language Processing: Learning & Optimization for NLP
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Security and Privacy: Large-scale security measurements
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies

Abstract

With the growing demand for applying large language models to downstream tasks, improving model alignment performance and efficiency has become crucial. Such a process involves selecting informative instructions from a candidate pool. However, due to the complexity of instruction set distributions, the key factors driving the performance of aligned models remain unclear. As a result, current instruction set refinement methods fail to improve performance as the instruction pool expands continuously. To address this issue, we first investigate the key factors that influence the relationship between instruction dataset distribution and aligned model performance. Based on these insights, we propose a novel instruction data selection method. We identify that the depth of instructions and the coverage of the semantic space are the crucial factors determining downstream performance, which could explain over 70% of the model loss on the development set. We then design an instruction selection algorithm to simultaneously maximize the depth and semantic coverage of the selected instructions. Experimental results demonstrate that, compared to state-of-the-art baseline methods, it can sustainably improve model performance at a faster pace and thus achieve emph{``Accelerated Scaling''}.

Problem

• Quantify instruction set coverage and depth
• Improve alignment performance and efficiency
• Address unclear key factors in instruction selection

Innovation

• Quantifying instruction depth and coverage
• Maximizing semantic space and instruction depth
• Algorithm for accelerated scaling of alignment