{"id":"https://openalex.org/W7163222734","doi":"https://doi.org/10.1109/access.2026.3699357","title":"RRLoRA: Refactorized Low-Rank Adaptation With Learning-Rate Restarts for Efficient Fine-Tuning","display_name":"RRLoRA: Refactorized Low-Rank Adaptation With Learning-Rate Restarts for Efficient Fine-Tuning","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7163222734","doi":"https://doi.org/10.1109/access.2026.3699357"},"language":"en","primary_location":{"id":"doi:10.1109/access.2026.3699357","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3699357","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/access.2026.3699357","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5070945903","display_name":"Mingzhe Yu","orcid":null},"institutions":[{"id":"https://openalex.org/I146399215","display_name":"University of Tsukuba","ror":"https://ror.org/02956yf07","country_code":"JP","type":"education","lineage":["https://openalex.org/I146399215"]},{"id":"https://openalex.org/I24254649","display_name":"Tsukuba University of Technology","ror":"https://ror.org/057bx9y85","country_code":"JP","type":"education","lineage":["https://openalex.org/I24254649"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Mingzhe Yu","raw_affiliation_strings":["Graduate School of Science and Technology, University of Tsukuba, Tsukuba, Ibaraki, Japan"],"raw_orcid":"https://orcid.org/0009-0005-4392-1226","affiliations":[{"raw_affiliation_string":"Graduate School of Science and Technology, University of Tsukuba, Tsukuba, Ibaraki, Japan","institution_ids":["https://openalex.org/I24254649","https://openalex.org/I146399215"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5137615333","display_name":"Osamu Tatebe","orcid":null},"institutions":[{"id":"https://openalex.org/I146399215","display_name":"University of Tsukuba","ror":"https://ror.org/02956yf07","country_code":"JP","type":"education","lineage":["https://openalex.org/I146399215"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Osamu Tatebe","raw_affiliation_strings":["Center for Computational Sciences, University of Tsukuba, Tsukuba, Ibaraki, Japan"],"raw_orcid":"https://orcid.org/0000-0003-4714-2164","affiliations":[{"raw_affiliation_string":"Center for Computational Sciences, University of Tsukuba, Tsukuba, Ibaraki, Japan","institution_ids":["https://openalex.org/I146399215"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.90132834,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":null,"biblio":{"volume":"14","issue":null,"first_page":"84922","last_page":"84942"},"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":0.4417000114917755,"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":0.4417000114917755,"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/T10860","display_name":"Speech and Audio Processing","score":0.07680000364780426,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T10283","display_name":"Hearing Loss and Rehabilitation","score":0.06960000097751617,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/algorithm-design","display_name":"Algorithm design","score":0.2883000075817108},{"id":"https://openalex.org/keywords/adaptation","display_name":"Adaptation (eye)","score":0.2752000093460083},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.259799987077713},{"id":"https://openalex.org/keywords/convergence","display_name":"Convergence (economics)","score":0.24289999902248383},{"id":"https://openalex.org/keywords/probability-density-function","display_name":"Probability density function","score":0.24160000681877136}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7414000034332275},{"id":"https://openalex.org/C106516650","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm design","level":2,"score":0.2883000075817108},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.2752000093460083},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.26409998536109924},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.259799987077713},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.24729999899864197},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.24289999902248383},{"id":"https://openalex.org/C197055811","wikidata":"https://www.wikidata.org/wiki/Q207522","display_name":"Probability density function","level":2,"score":0.24160000681877136},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.2409999966621399},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2320999950170517}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2026.3699357","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3699357","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:158f22ce12474cb582b88f4cd9d8e97d","is_oa":true,"landing_page_url":"https://doaj.org/article/158f22ce12474cb582b88f4cd9d8e97d","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 14, Pp 84922-84942 (2026)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2026.3699357","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3699357","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G3633360255","display_name":null,"funder_award_id":"JPNP20017","funder_id":"https://openalex.org/F4320321034","funder_display_name":"New Energy and Industrial Technology Development Organization"},{"id":"https://openalex.org/G7181494585","display_name":null,"funder_award_id":"JP22H00509","funder_id":"https://openalex.org/F4320334764","funder_display_name":"Japan Society for the Promotion of Science"}],"funders":[{"id":"https://openalex.org/F4320321034","display_name":"New Energy and Industrial Technology Development Organization","ror":"https://ror.org/0055k7a87"},{"id":"https://openalex.org/F4320334764","display_name":"Japan Society for the Promotion of Science","ror":"https://ror.org/00hhkn466"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Low-rank":[0],"adaptation":[1],"(LoRA)":[2],"has":[3,18],"become":[4],"a":[5,44,54,61,68,81,102,126],"standard":[6,62],"parameter-efficient":[7],"fine-tuning":[8,52,71],"technique":[9],"for":[10],"adapting":[11],"large":[12],"foundation":[13],"models.":[14],"However,":[15],"prior":[16],"work":[17],"shown":[19],"that":[20,49,141],"LoRA":[21,51,98,121],"training":[22,154],"dynamics":[23],"and":[24,31,39,73,91,134,176,189],"final":[25],"performance":[26,144],"are":[27],"sensitive":[28],"to":[29,107,117],"initialization,":[30,66],"state-of-the-art":[32],"data-driven":[33],"initializations":[34],"often":[35],"incur":[36],"significant":[37],"runtime":[38],"engineering":[40],"overhead.We":[41],"propose":[42],"RRLoRA,":[43],"simple":[45],"training-time":[46],"refactorization":[47],"approach":[48],"improves":[50,143],"without":[53],"separate":[55],"initialization":[56],"stage.":[57],"RRLoRA":[58,142],"starts":[59],"from":[60],"random":[63],"or":[64],"weight-driven":[65],"performs":[67],"short":[69],"warmup":[70],"phase,":[72],"then":[74],"periodically":[75],"refactorizes":[76],"each":[77],"adapter":[78,89],"by":[79],"computing":[80],"rank-r":[82],"truncated":[83],"singular":[84],"value":[85],"decomposition":[86],"of":[87,149],"the":[88,93,97,119,146],"product":[90],"re-encoding":[92],"factors":[94],"back":[95],"into":[96],"matrices,":[99],"coupled":[100],"with":[101],"synchronized":[103],"learning-rate":[104],"hard":[105],"restart":[106],"stabilize":[108],"optimization.":[109],"We":[110],"further":[111],"introduce":[112],"an":[113],"optional":[114],"singular-value-based":[115],"heuristic":[116],"calibrate":[118],"layer-wise":[120],"scaling":[122],"factor":[123],"\u03b1":[124],"under":[125,152],"fixed":[127],"rank.":[128],"Experiments":[129],"across":[130],"language":[131],"understanding,":[132],"reasoning,":[133],"open-ended":[135],"vision":[136],"question":[137],"answering":[138],"benchmarks":[139],"show":[140],"in":[145,162,170,179,193],"vast":[147],"majority":[148],"evaluated":[150],"settings":[151],"matched":[153],"budgets.":[155],"Representative":[156],"gains":[157],"include":[158],"0.38\u20131.03":[159],"percentage":[160,168],"points":[161,169,178],"average":[163],"accuracy":[164,173],"on":[165,174,182],"Commonsense170K,":[166],"0.03\u20133.93":[167],"normalized":[171],"exact-match":[172],"ChartQA,":[175],"0.039\u20130.676":[177],"LLM-judge":[180],"score":[181],"Kvasir-VQA-x1,":[183],"while":[184],"adding":[185],"negligible":[186],"wall-clock":[187],"overhead":[188],"only":[190],"minor":[191],"changes":[192],"peak":[194],"allocated":[195],"GPU":[196],"memory.":[197]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-06-03T00:00:00"}
