[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-85697-en":3,"doc-seo-85697-105":29,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},85697,137441390410,"Hazel","https://ap-avatar.wpscdn.com/avatar/2000252f4ab5702993?_k=1776741390130283984",8,"Research & Report","RSLoRA Training-free Rank Allocation for LoRA via Representational Sensitivity Probing","Low-Rank Adaptation (LoRA) is widely used for parameter-efficient fine-tuning, yet uniform rank assignment across layers neglects functional heterogeneity and reduces task quality. Existing rank allocators face a trade-off: training-based approaches incur heavy overhead, while pre-allocation heuristics miss dynamic task-specific representation changes. RSLoRA (Representational Sensitivity LoRA) is a training-free, gradient-free framework that probes activation-space geometry via virtual representational perturbations, using Effective Rank and Fréchet Distance to allocate higher ranks to high-sensitivity modules. Evaluations show consistent gains over methods such as AdaLoRA and GoRA across mainstream benchmarks.","RSLoRA: Training-free Rank Allocation for LoRA via Representational Sensitivity Probing  \nJiaqi Liu  \nSchool of Computer Science and Technology Dalian University of Technology  \nHaidong Kang∗  \nHebei Key Laboratory of Marine Perception Network and Data Processing Northeastern University  \narXiv :2607 .09757v 1 [ cs .CV] 5 Jul 2026  \nQihui Zhao  \nSchool of Computer and Communication Engineering Northeastern University  \nGuo Yu  \nInstitute of Intelligent Manufacturing Nanjing Tech University  \nAbstract  \nLow-Rank Adaptation (LoRA) has become a cornerstone of parameter-efficient fine-tuning (PEFT); however, the conventional practice of uniform rank assignment ignores the functional heterogeneity of neural layers. Existing rank allocation methods typically struggle with a trade-off between computational intensity and heuristic simplicity: training-based methods suffer from prohibitive overhead, while pre-allocation methods fail to capture the dynamic task-specific representation manifold. In this paper, we propose RSLoRA (Representational Sensitivity LoRA), a training-free and gradient-free rank allocator driven by activation-space geometry. We identify a \"sensitivity regime shift\" across layers, observing that static weight analysis and local gradients are insufficient to reflect how updates reshape a model’s internal representations. To address this, RSLoRA introducesa virtual representational probing mechanism. By simulating adaptation through structured low-rank noise and measuring the resulting manifold displacement by using Effective Rank and Fréchet Distance, we identify high-sensitivity modules that require higher rank capacity. Our framework effectively bridges the gap between expert-crafted heuristics and actual representational impact. Extensive evaluations demonstrate that RSLoRA consistently outperforms state-of-the-art allocators (e.g., AdaLoRA, GoRA) across mainstream benchmarks. By eliminating the need for iterative training-time adjustments and backward gradients, RSLoRA provides a highly efficient, robust, and representation-aware solution for large-scale model adaptation.  \n1 Introduction  \nDeep neural networks, particularly Large Language Models (LLMs), have become indispensable in modern natural language processing applications. However, when fine-tuning these models for specific downstream tasks, they often encounter severe computational and storage challenges due to the massive number of parameters. This tension motivates the development of parameter-efficient finetuning (PEFT) techniques. Although Low-Rank Adaptation (LoRA) [1] provides a straightforward  \n∗ Correspondence to: \u003C[kanghaidong@qhd.neu.edu.cn](kanghaidong@qhd.neu.edu.cn)>  \nPreprint.  \nsolution by using low-rank decomposition matrices, its uniform rank assignment across all layers usually leads to suboptimal performance due to the functional heterogeneity of different modules. Adaptive rank allocation methods [2, 3], in contrast, adjust ranks according to layer importance and thus achieve a better balance between efficiency and accuracy. Existing training-based allocation methods [4, 5] further attempt to learn rank distributions dynamically, yet their high computational cost and complex regularization severely limit practical deployment for large-scale models. As a response, pre-allocation methods (e.g., AIRA [3], SR-LoRA [6], GoRA [7]) avoid huge training costs, although they still rely on heuristic metrics that lack a unified understanding of representational impact.  \nChallenges. Although these adaptive rank allocation approaches have moved the field forward, they still face two essential challenges. First, they remain strongly dependent on handcrafted heuristics. As summarized in Section 2, existing proxies are constructed through expert knowledge, such as SVD-based importance scoring in AdaLoRA [2] or manually selected weight–activation statistics, such as \"outlier\" identification in AIRA [3] . Designing such rules usually involves extens","cbCaie2klXT41auZ","https://ap.wps.com/l/cbCaie2klXT41auZ","pdf",343650,1,16,"English","en",105,"# Abstract\n# Introduction\n## Challenges\n## Motivations\n## Contributions","[{\"question\":\"What problem does RSLoRA address in LoRA fine-tuning?\",\"answer\":\"RSLoRA targets the suboptimal performance caused by uniform LoRA rank assignment across layers, which ignores functional heterogeneity and fails to reflect how updates change internal representations.\"},{\"question\":\"How does RSLoRA allocate ranks without training or gradients?\",\"answer\":\"RSLoRA uses a training-free, gradient-free virtual representational probing mechanism that simulates structured low-rank adaptation noise and measures manifold displacement using Effective Rank and Fréchet Distance.\"},{\"question\":\"What are RSLoRA’s key benefits over existing rank allocators like AdaLoRA and GoRA?\",\"answer\":\"RSLoRA bridges expert heuristics and representational impact by discovering high-sensitivity modules from activation-space geometry, eliminating iterative training-time adjustments and backward gradients while improving benchmark performance.\"}]",1784205668,40,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":27},"rslora-training-free-rank-allocation-for-lora-via-representational-sensitivity-probing","",{"@graph":35,"@context":85},[36,53,68],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/rslora-training-free-rank-allocation-for-lora-via-representational-sensitivity-probing/85697/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-17","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does RSLoRA address in LoRA fine-tuning?","Question",{"text":75,"@type":76},"RSLoRA targets the suboptimal performance caused by uniform LoRA rank assignment across layers, which ignores functional heterogeneity and fails to reflect how updates change internal representations.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does RSLoRA allocate ranks without training or gradients?",{"text":80,"@type":76},"RSLoRA uses a training-free, gradient-free virtual representational probing mechanism that simulates structured low-rank adaptation noise and measures manifold displacement using Effective Rank and Fréchet Distance.",{"name":82,"@type":73,"acceptedAnswer":83},"What are RSLoRA’s key benefits over existing rank allocators like AdaLoRA and GoRA?",{"text":84,"@type":76},"RSLoRA bridges expert heuristics and representational impact by discovering high-sensitivity modules from activation-space geometry, eliminating iterative training-time adjustments and backward gradients 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