{"id":"https://openalex.org/W7161810753","doi":"https://doi.org/10.48550/arxiv.2605.19018","title":"LoRA vs. Full Fine-Tuning: A Theoretical Perspective","display_name":"LoRA vs. Full Fine-Tuning: A Theoretical Perspective","publication_year":2026,"publication_date":"2026-05-18","ids":{"openalex":"https://openalex.org/W7161810753","doi":"https://doi.org/10.48550/arxiv.2605.19018"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.19018","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.19018","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.2605.19018","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5136565264","display_name":"Ali Zindari","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zindari, Ali","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076334995","display_name":"Rotem Mulayoff","orcid":"https://orcid.org/0000-0002-5991-0602"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mulayoff, Rotem","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5136473183","display_name":"Sebastian U. Stich","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Stich, Sebastian U.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.6621999740600586,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.6621999740600586,"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/T11612","display_name":"Stochastic Gradient Optimization Techniques","score":0.10289999842643738,"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/T11448","display_name":"Face recognition and analysis","score":0.024399999529123306,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/overdetermined-system","display_name":"Overdetermined system","score":0.7786999940872192},{"id":"https://openalex.org/keywords/perspective","display_name":"Perspective (graphical)","score":0.5644000172615051},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.5206999778747559},{"id":"https://openalex.org/keywords/simple","display_name":"Simple (philosophy)","score":0.462799996137619},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.4618000090122223},{"id":"https://openalex.org/keywords/rank","display_name":"Rank (graph theory)","score":0.44519999623298645},{"id":"https://openalex.org/keywords/underdetermined-system","display_name":"Underdetermined system","score":0.40880000591278076},{"id":"https://openalex.org/keywords/linear-regression","display_name":"Linear regression","score":0.3953000009059906}],"concepts":[{"id":"https://openalex.org/C81901731","wikidata":"https://www.wikidata.org/wiki/Q331348","display_name":"Overdetermined system","level":2,"score":0.7786999940872192},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6725000143051147},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.5644000172615051},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.5206999778747559},{"id":"https://openalex.org/C2780586882","wikidata":"https://www.wikidata.org/wiki/Q7520643","display_name":"Simple (philosophy)","level":2,"score":0.462799996137619},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.4618000090122223},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.44519999623298645},{"id":"https://openalex.org/C179690561","wikidata":"https://www.wikidata.org/wiki/Q4316110","display_name":"Underdetermined system","level":2,"score":0.40880000591278076},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.3953000009059906},{"id":"https://openalex.org/C45340560","wikidata":"https://www.wikidata.org/wiki/Q215382","display_name":"Disjoint sets","level":2,"score":0.3917999863624573},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.39160001277923584},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.3824999928474426},{"id":"https://openalex.org/C163175372","wikidata":"https://www.wikidata.org/wiki/Q3339222","display_name":"Linear model","level":2,"score":0.3732999861240387},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3440000116825104},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.3303999900817871},{"id":"https://openalex.org/C2776207758","wikidata":"https://www.wikidata.org/wiki/Q5303302","display_name":"Downstream (manufacturing)","level":2,"score":0.32760000228881836},{"id":"https://openalex.org/C25343380","wikidata":"https://www.wikidata.org/wiki/Q277521","display_name":"Relation (database)","level":2,"score":0.3239000141620636},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.3237000107765198},{"id":"https://openalex.org/C2777267654","wikidata":"https://www.wikidata.org/wiki/Q3519023","display_name":"Test (biology)","level":2,"score":0.31869998574256897},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.310699999332428},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2892000079154968},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.2842000126838684},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.2786000072956085},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.27790001034736633},{"id":"https://openalex.org/C21200559","wikidata":"https://www.wikidata.org/wiki/Q7451068","display_name":"Sensitivity (control systems)","level":2,"score":0.2700999975204468},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2696000039577484},{"id":"https://openalex.org/C41045048","wikidata":"https://www.wikidata.org/wiki/Q202843","display_name":"Linear programming","level":2,"score":0.2651999890804291}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.19018","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.19018","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.2605.19018","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.19018","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":{"Fine-tuning":[0],"adapts":[1],"a":[2,9,58,131],"pre-trained":[3],"model":[4,146],"to":[5,34],"downstream":[6,111],"tasks":[7,112],"using":[8,130],"small":[10,133],"amount":[11],"of":[12,44,70,122],"labeled":[13],"data.":[14],"Low-Rank":[15],"Adaptation":[16],"(LoRA)":[17],"is":[18,46,113],"an":[19],"efficient":[20],"fine-tuning":[21,86,102],"method":[22],"that":[23,69,97,160],"reduces":[24],"memory":[25],"and":[26,63,90,109,164],"computation":[27],"costs":[28],"while":[29],"often":[30],"achieving":[31],"performance":[32],"close":[33],"full":[35,71,85,101],"fine-tuning.":[36,72],"Despite":[37],"its":[38,65],"widespread":[39],"use,":[40],"the":[41,104,107,110,120,161],"theoretical":[42,152],"behavior":[43],"LoRA":[45,56,79,98,123],"not":[47],"yet":[48],"well":[49],"understood.":[50],"In":[51],"this":[52],"paper,":[53],"we":[54,149],"study":[55],"in":[57,77,87,139],"simple":[59],"linear":[60,168],"regression":[61],"setting":[62],"compare":[64],"excess":[66,82],"risk":[67,83],"with":[68,154],"Our":[73],"analysis":[74],"identifies":[75],"regimes":[76],"which":[78],"achieves":[80],"lower":[81],"than":[84],"both":[88],"overdetermined":[89],"underdetermined":[91],"settings.":[92],"Specifically,":[93],"our":[94,151],"theory":[95],"predicts":[96],"can":[99,135],"outperform":[100],"when":[103],"difference":[105],"between":[106],"pretraining":[108],"effectively":[114],"low-rank.":[115],"We":[116],"further":[117],"show":[118],"how":[119],"choice":[121],"rank":[124,134],"affects":[125],"generalization":[126],"performance,":[127],"explaining":[128],"why":[129],"very":[132],"improve":[136],"test":[137],"accuracy":[138],"certain":[140],"settings,":[141],"even":[142],"though":[143],"it":[144],"limits":[145],"expressivity.":[147],"Finally,":[148],"support":[150],"results":[153],"experiments":[155],"on":[156],"practical":[157],"tasks,":[158],"suggesting":[159],"identified":[162],"tradeoffs":[163],"insights":[165],"extend":[166],"beyond":[167],"regression.":[169]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-05-21T00:00:00"}