根据模型能力动态调整训练顺序，让大模型学得更快更好
Teaching According to Talents! Instruction Tuning LLMs with Competence-Aware Curriculum Learning
- 用模型当前能力动态选题，不按固定难度排
- 在多个数据集上比现有方法提升10%以上效果
- 适合追求高效训练的大模型研究者和工程师
高效指令微调旨在提升大语言模型在特定指令数据集上的最终性能。课程学习作为一种典型的数据组织策略，在指令微调中已展现出初步成效。然而，现有课程微调方法存在课程僵化问题，仅依赖静态启发式难度指标，无法随模型能力演化进行调整，导致学习路径固定且可能次优。为此，本文提出一种基于能力感知的多视角课程指令微调框架CAMPUS。该框架具备三大优势：（1）动态选择子课程；（2）根据模型能力自适应调整课程进度；（3）支持多种难度维度的调度策略。大量实验证明，相较于其他先进基线方法，CAMPUS在高效指令微调任务中表现更优。
原文摘要 · Abstract (English)
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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