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

2025-09-17

AI Summary

Existing curriculum learning methods rely on static difficulty metrics, failing to accommodate the dynamic capability evolution of large language models (LLMs) during instruction tuning—resulting in rigid, suboptimal learning trajectories. To address this, we propose CAMPUS, the first capability-aware dynamic curriculum learning framework for instruction tuning. CAMPUS continuously monitors the model’s multi-dimensional capabilities, integrates multi-perspective difficulty estimation, and introduces dynamic sub-curriculum selection alongside adaptive difficulty scheduling—thereby enabling personalized, evolution-aware instruction fine-tuning. Extensive experiments demonstrate that CAMPUS consistently outperforms state-of-the-art curriculum learning baselines on major benchmarks—including AlpacaEval and MT-Bench—with average improvements of +2.1–3.8 points—validating its effectiveness and generalizability across diverse LLMs and instruction datasets.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Planning, Routing, and Scheduling: Planning with Language Models
• Natural Language Processing: (Large) Language Models

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information

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.

Problem

• Addressing curriculum rigidity in instruction tuning methods
• Adapting to evolving model capabilities during training
• Improving learning trajectory with dynamic difficulty scheduling

Innovation

• Dynamic sub-curriculum selection based on model competence
• Competency-aware adjustment of curriculum scheduling
• Multiple difficulty perspectives for instruction tuning