Adapting large language models to multiple tasks can cause cross-skill interference, where improvements for one skill degrade another. While methods such as LoRA impose orthogonality constraints at the weight level, they do not fully address interference in hidden-state representations. We propose Compositional Subspace Representation Fine-tuning (CS-ReFT), a novel representation-based approach that learns multiple orthonormal subspace transformations, each specializing in a distinct skill, and composes them via a lightweight router. By isolating these subspace edits in the hidden state, rather than weight matrices, CS-ReFT prevents cross-task conflicts more effectively. On the AlpacaEval benchmark, applying CS-ReFT to Llama-2-7B achieves a 93.94% win rate, surpassing GPT-3.5 Turbo (86.30%) while requiring only 0.0098% of model parameters. These findings show that specialized representation edits, composed via a simple router, significantly enhance multi-task instruction following with minimal overhead.

本研究针对大语言模型在多任务适应中可能导致的跨技能干扰问题，提出了一种新的基于表示的方法CS-ReFT。该方法通过学习多个正交子空间变换，有效地隔离隐藏状态中的子空间编辑，从而提高了多任务指令跟随的能力。研究表明，CS-ReFT能够有效避免跨任务冲突，显著提升性能，且所需模型参数极少。

适应性大语言模型的组合子空间表示微调