{"id":"https://openalex.org/W7160427009","doi":"https://doi.org/10.48550/arxiv.2605.03724","title":"Rethinking the Rank Threshold for LoRA Fine-Tuning","display_name":"Rethinking the Rank Threshold for LoRA Fine-Tuning","publication_year":2026,"publication_date":"2026-05-05","ids":{"openalex":"https://openalex.org/W7160427009","doi":"https://doi.org/10.48550/arxiv.2605.03724"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.03724","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.03724","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.03724","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5135495035","display_name":"Juneyoung Park","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Park, Juneyoung","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":["https://openalex.org/A5135495035"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11612","display_name":"Stochastic Gradient Optimization Techniques","score":0.40119999647140503,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11612","display_name":"Stochastic Gradient Optimization Techniques","score":0.40119999647140503,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12808","display_name":"Ferroelectric and Negative Capacitance Devices","score":0.27959999442100525,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.03709999844431877,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/rank","display_name":"Rank (graph theory)","score":0.7293999791145325},{"id":"https://openalex.org/keywords/dimension","display_name":"Dimension (graph theory)","score":0.6549000144004822},{"id":"https://openalex.org/keywords/binary-number","display_name":"Binary number","score":0.47940000891685486},{"id":"https://openalex.org/keywords/spurious-relationship","display_name":"Spurious relationship","score":0.44670000672340393},{"id":"https://openalex.org/keywords/low-rank-approximation","display_name":"Low-rank approximation","score":0.4171999990940094},{"id":"https://openalex.org/keywords/maxima-and-minima","display_name":"Maxima and minima","score":0.4018000066280365},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.3815000057220459},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.37470000982284546},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.3391000032424927}],"concepts":[{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.7566999793052673},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.7293999791145325},{"id":"https://openalex.org/C33676613","wikidata":"https://www.wikidata.org/wiki/Q13415176","display_name":"Dimension (graph theory)","level":2,"score":0.6549000144004822},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.5048999786376953},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.47940000891685486},{"id":"https://openalex.org/C97256817","wikidata":"https://www.wikidata.org/wiki/Q1462316","display_name":"Spurious relationship","level":2,"score":0.44670000672340393},{"id":"https://openalex.org/C90199385","wikidata":"https://www.wikidata.org/wiki/Q6692777","display_name":"Low-rank approximation","level":3,"score":0.4171999990940094},{"id":"https://openalex.org/C186633575","wikidata":"https://www.wikidata.org/wiki/Q845060","display_name":"Maxima and minima","level":2,"score":0.4018000066280365},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.3815000057220459},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.37470000982284546},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.350600004196167},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.3391000032424927},{"id":"https://openalex.org/C138187205","wikidata":"https://www.wikidata.org/wiki/Q131251","display_name":"Tangent","level":2,"score":0.33160001039505005},{"id":"https://openalex.org/C2778029271","wikidata":"https://www.wikidata.org/wiki/Q5421931","display_name":"Extension (predicate logic)","level":2,"score":0.3310999870300293},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.3255999982357025},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.3231000006198883},{"id":"https://openalex.org/C77553402","wikidata":"https://www.wikidata.org/wiki/Q13222579","display_name":"Upper and lower bounds","level":2,"score":0.32089999318122864},{"id":"https://openalex.org/C118615104","wikidata":"https://www.wikidata.org/wiki/Q121416","display_name":"Discrete mathematics","level":1,"score":0.31299999356269836},{"id":"https://openalex.org/C529865628","wikidata":"https://www.wikidata.org/wiki/Q1790740","display_name":"Manifold (fluid mechanics)","level":2,"score":0.30790001153945923},{"id":"https://openalex.org/C126780896","wikidata":"https://www.wikidata.org/wiki/Q899871","display_name":"Distortion (music)","level":4,"score":0.3068000078201294},{"id":"https://openalex.org/C117251300","wikidata":"https://www.wikidata.org/wiki/Q1849855","display_name":"Parametric statistics","level":2,"score":0.30070000886917114},{"id":"https://openalex.org/C2776639384","wikidata":"https://www.wikidata.org/wiki/Q840396","display_name":"Ideal (ethics)","level":2,"score":0.2623000144958496},{"id":"https://openalex.org/C70710897","wikidata":"https://www.wikidata.org/wiki/Q680081","display_name":"Separable space","level":2,"score":0.2619999945163727},{"id":"https://openalex.org/C204707403","wikidata":"https://www.wikidata.org/wiki/Q1152398","display_name":"Canonical form","level":2,"score":0.26179999113082886},{"id":"https://openalex.org/C102366305","wikidata":"https://www.wikidata.org/wiki/Q1097688","display_name":"Nonparametric statistics","level":2,"score":0.2606000006198883},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.2533999979496002}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.03724","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.03724","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.03724","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.03724","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"A":[0],"recent":[1],"landscape":[2],"analysis":[3],"of":[4,28],"LoRA":[5,22,103],"fine-tuning":[6],"in":[7,56,70,91,135],"the":[8,21,26,65,81,96,101,136,139,143,157,164,194,203,221],"neural":[9],"tangent":[10],"kernel":[11],"regime":[12],"establishes":[13],"a":[14,107,148],"sufficient":[15],"condition":[16,45],"$r(r+1)/2":[17],">":[18,115,173],"KN$":[19,118],"on":[20,39,131,187,200,217],"rank":[23,83,144,189,205],"$r$":[24],"for":[25,48,64,88,166,171],"absence":[27],"spurious":[29],"local":[30],"minima":[31],"under":[32,123],"squared-error":[33],"loss,":[34],"prescribing":[35],"$r":[36,85,128,197],"\\geq":[37],"12$":[38],"canonical":[40,132],"few-shot":[41],"RoBERTa":[42],"setups.":[43,133],"The":[44,212],"is":[46,160,163,191,224],"stated":[47],"general":[49],"output":[50],"dimension":[51,105],"$K$,":[52],"so":[53],"its":[54,61],"sharpness":[55],"any":[57],"particular":[58],"regime,":[59],"and":[60,184],"practical":[62],"implication":[63],"cross-entropy":[66,137],"loss":[67],"actually":[68],"used":[69],"fine-tuning,":[71],"are":[72,215],"open.":[73],"We":[74],"give":[75],"three":[76,181],"results":[77],"that":[78],"together":[79],"reduce":[80],"prescribed":[82],"to":[84,226],"=":[86,129,198],"1$":[87,130],"binary":[89,167,179],"classification":[90,168],"this":[92],"regime.":[93],"First,":[94],"replacing":[95],"symmetric":[97],"Sard-form":[98],"count":[99],"with":[100,119,193],"non-symmetric":[102],"manifold":[104],"yields":[106],"strictly":[108],"weaker":[109],"capacity":[110],"requirement,":[111],"$r(m+n)":[112],"-":[113],"r^2":[114],"C^*":[116],"\\cdot":[117],"$C^*":[120],"\\approx":[121],"1.35$":[122],"Gaussian-iid":[124],"features,":[125],"satisfied":[126],"at":[127,185],"Second,":[134],"setting":[138],"Polyak--\u0141ojasiewicz":[140],"inequality":[141],"removes":[142],"threshold":[145],"entirely.":[146],"Third,":[147],"Rademacher-complexity":[149],"bound":[150],"predicts":[151],"rank-one":[152],"variance":[153],"optimality":[154],"precisely":[155],"when":[156],"bias":[158],"term":[159],"saturated,":[161],"which":[162],"case":[165],"but":[169],"not":[170],"$K":[172],"2$.":[174],"Empirically,":[175],"across":[176],"four":[177],"GLUE-style":[178],"tasks,":[180],"encoder":[182],"architectures,":[183],"scale":[186],"RoBERTa-large,":[188],"one":[190],"competitive":[192],"existing":[195],"prescription":[196],"12$;":[199],"multi-class":[201,222],"MNLI":[202],"optimal":[204],"shifts":[206],"above":[207],"one,":[208],"also":[209],"as":[210],"predicted.":[211],"binary-regime":[213],"guarantees":[214],"conditional":[216],"standard":[218],"NTK":[219],"assumptions;":[220],"extension":[223],"left":[225],"future":[227],"work.":[228]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-05-07T00:00:00"}