Teaching According to Talents! Instruction Tuning LLMs with Competence-Aware Curriculum Learning

• URL: http://arxiv.org/abs/2509.13790v2
• Date: Mon, 03 Nov 2025 09:06:01 GMT
• Title: Teaching According to Talents! Instruction Tuning LLMs with Competence-Aware Curriculum Learning
• Authors: Yangning Li, Tingwei Lu, Yinghui Li, Yankai Chen, Wei-Chieh Huang, Wenhao Jiang, Hui Wang, Hai-Tao Zheng, Philip S. Yu,
• Abstract summary: This paper presents a Competence-Aware Multi-Perspective cUrriculum inStruction tuning framework termed CAMPUS.<n> CAMPUS offers several advantages: Dynamic selection for sub-curriculum, competency-aware adjustment to the curriculum schedule, and multiple difficulty-based scheduling.
• Score: 64.92967672226534
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Efficient instruction tuning aims to enhance the ultimate performance of large language models (LLMs) trained on a given instruction dataset. Curriculum learning as a typical data organization strategy has shown preliminary effectiveness in instruction tuning. However, current curriculum tuning methods suffer from the curriculum rigidity, since they rely solely on static heuristic difficulty metrics. These methods fail to adapt to the evolving capabilities of models during training, resulting in a fixed and potentially sub-optimal learning trajectory. To address the issue, Competence-Aware Multi-Perspective cUrriculum inStruction tuning framework termed CAMPUS is proposed. CAMPUS offers several advantages: (1) Dynamic selection for sub-curriculum. (2) Competency-aware adjustment to the curriculum schedule. (3) Multiple difficulty-based scheduling. Extensive experiments prove the superior performance of CAMPUS, compared to other state-of-the-art baselines for efficient instruction tuning.

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