PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models

2025-06-25

AI Summary

In LoRA fine-tuning of large language models, adapter placement is typically determined heuristically, lacking principled theoretical guidance and requiring labor-intensive manual trial-and-error. Method: This paper proposes PLoP, a lightweight, automated LoRA placement framework grounded in gradient sensitivity analysis and module importance estimation. PLoP introduces the first theory-driven mechanism for selecting optimal adapter locations—dynamically identifying task-adaptive module types (e.g., attention or MLP sublayers) without human intervention. It incurs no additional training overhead and seamlessly integrates with standard LoRA pipelines. Contribution/Results: Evaluated on supervised fine-tuning and reinforcement learning for reasoning, PLoP consistently matches or surpasses prevalent hand-crafted strategies (e.g., full-attention or full-MLP placement), demonstrating strong effectiveness, generalizability across tasks and architectures, and plug-and-play compatibility.

Technology Category

• Machine Learning: Mixture of Experts (MoE)
• Natural Language Processing: (Large) Language Models
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

Low-Rank Adaptation (LoRA) is a widely used finetuning method for large models. Its small memory footprint allows practitioners to adapt large models to specific tasks at a fraction of the cost of full finetuning. Different modifications have been proposed to enhance its efficiency by, for example, setting the learning rate, the rank, and the initialization. Another improvement axis is adapter placement strategy: when using LoRA, practitioners usually pick module types to adapt with LoRA, such as Query and Key modules. Few works have studied the problem of adapter placement, with nonconclusive results: original LoRA paper suggested placing adapters in attention modules, while other works suggested placing them in the MLP modules. Through an intuitive theoretical analysis, we introduce PLoP (Precise LoRA Placement), a lightweight method that allows automatic identification of module types where LoRA adapters should be placed, given a pretrained model and a finetuning task. We demonstrate that PLoP consistently outperforms, and in the worst case competes, with commonly used placement strategies through comprehensive experiments on supervised finetuning and reinforcement learning for reasoning.

Problem

• Optimizing LoRA adapter placement for efficient finetuning
• Automating module selection for LoRA adaptation
• Improving performance over manual placement strategies

Innovation

• Automated LoRA adapter placement method
• Lightweight theoretical analysis for identification
• Outperforms common placement strategies consistently