提出CoBa方法，让大模型多任务训练更均衡高效。
CoBa: Convergence Balancer for Multitask Finetuning of Large Language Models
- 动态调整任务权重，用收敛分数控制训练进度
- 在三个数据集上提升性能最高达13%
- 计算开销小，适合资源有限的多任务微调
多任务学习（MTL）通过单一模型提升大语言模型在多个任务上的表现与泛化能力，是一种高效的替代方案。然而现有方法或计算开销大，或无法保证各任务同步收敛。本文提出CoBa，一种低开销的MTL方法，利用相对收敛分数（RCS）、绝对收敛分数（ACS）和发散因子（DF）动态调节任务权重，使各任务验证损失以相近速度收敛，避免个别任务偏离。实验在三个不同数据集上验证，该方法不仅实现任务收敛均衡，还使模型性能相较次优基线最高提升13%。代码已开源：https://github.com/codefuse-ai/MFTCoder。
原文摘要 · Abstract (English)
Multi-task learning (MTL) benefits the fine-tuning of large language models (LLMs) by providing a single model with improved performance and generalization ability across tasks, presenting a resource-efficient alternative to developing separate models for each task. Yet, existing MTL strategies for LLMs often fall short by either being computationally intensive or failing to ensure simultaneous task convergence. This paper presents CoBa, a new MTL approach designed to effectively manage task convergence balance with minimal computational overhead. Utilizing Relative Convergence Scores (RCS), Absolute Convergence Scores (ACS), and a Divergence Factor (DF), CoBa dynamically adjusts task weights during the training process, ensuring that the validation loss of all tasks progress towards convergence at an even pace while mitigating the issue of individual task divergence. The results of our experiments involving three disparate datasets underscore that this approach not only fosters equilibrium in task convergence but enhances the LLMs' performance by up to 13% relative to the second-best baselines. Code is open-sourced at https://github.com/codefuse-ai/MFTCoder.
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