{"id":"https://openalex.org/W7140188843","doi":"https://doi.org/10.48550/arxiv.2603.21725","title":"CurvZO: Adaptive Curvature-Guided Sparse Zeroth-Order Optimization for Efficient LLM Fine-Tuning","display_name":"CurvZO: Adaptive Curvature-Guided Sparse Zeroth-Order Optimization for Efficient LLM Fine-Tuning","publication_year":2026,"publication_date":"2026-03-23","ids":{"openalex":"https://openalex.org/W7140188843","doi":"https://doi.org/10.48550/arxiv.2603.21725"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.21725","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.21725","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","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.2603.21725","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Wang, Shuo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Shuo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Chen, Ziyu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Ziyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":null,"display_name":"Tang, Ming","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tang, Ming","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/T11206","display_name":"Model Reduction and Neural Networks","score":0.19910000264644623,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11206","display_name":"Model Reduction and Neural Networks","score":0.19910000264644623,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11612","display_name":"Stochastic Gradient Optimization Techniques","score":0.13910000026226044,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.053199999034404755,"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/scalability","display_name":"Scalability","score":0.5746999979019165},{"id":"https://openalex.org/keywords/convergence","display_name":"Convergence (economics)","score":0.5619000196456909},{"id":"https://openalex.org/keywords/optimization-problem","display_name":"Optimization problem","score":0.4832000136375427},{"id":"https://openalex.org/keywords/perturbation","display_name":"Perturbation (astronomy)","score":0.3939000070095062},{"id":"https://openalex.org/keywords/limiting","display_name":"Limiting","score":0.3783000111579895},{"id":"https://openalex.org/keywords/gradient-descent","display_name":"Gradient descent","score":0.34200000762939453},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.3328000009059906},{"id":"https://openalex.org/keywords/curvature","display_name":"Curvature","score":0.3310000002384186},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.31049999594688416}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6758999824523926},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.5746999979019165},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.5619000196456909},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4884999990463257},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.4832000136375427},{"id":"https://openalex.org/C177918212","wikidata":"https://www.wikidata.org/wiki/Q803623","display_name":"Perturbation (astronomy)","level":2,"score":0.3939000070095062},{"id":"https://openalex.org/C188198153","wikidata":"https://www.wikidata.org/wiki/Q1613840","display_name":"Limiting","level":2,"score":0.3783000111579895},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.35120001435279846},{"id":"https://openalex.org/C153258448","wikidata":"https://www.wikidata.org/wiki/Q1199743","display_name":"Gradient descent","level":3,"score":0.34200000762939453},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.3328000009059906},{"id":"https://openalex.org/C195065555","wikidata":"https://www.wikidata.org/wiki/Q214881","display_name":"Curvature","level":2,"score":0.3310000002384186},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.31049999594688416},{"id":"https://openalex.org/C157972887","wikidata":"https://www.wikidata.org/wiki/Q463359","display_name":"Convex optimization","level":3,"score":0.3066999912261963},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.30410000681877136},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3005000054836273},{"id":"https://openalex.org/C52740198","wikidata":"https://www.wikidata.org/wiki/Q1539564","display_name":"Importance sampling","level":3,"score":0.29809999465942383},{"id":"https://openalex.org/C147764199","wikidata":"https://www.wikidata.org/wiki/Q6865248","display_name":"Minification","level":2,"score":0.29739999771118164},{"id":"https://openalex.org/C124066611","wikidata":"https://www.wikidata.org/wiki/Q28684319","display_name":"Sparse approximation","level":2,"score":0.29339998960494995},{"id":"https://openalex.org/C57691317","wikidata":"https://www.wikidata.org/wiki/Q1289248","display_name":"Scalar (mathematics)","level":2,"score":0.2892000079154968},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.28450000286102295},{"id":"https://openalex.org/C2987595161","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Optimization algorithm","level":2,"score":0.28139999508857727},{"id":"https://openalex.org/C155032097","wikidata":"https://www.wikidata.org/wiki/Q798503","display_name":"Backpropagation","level":3,"score":0.28049999475479126},{"id":"https://openalex.org/C149672232","wikidata":"https://www.wikidata.org/wiki/Q337048","display_name":"Adaptive optimization","level":2,"score":0.2782999873161316},{"id":"https://openalex.org/C164752517","wikidata":"https://www.wikidata.org/wiki/Q5570875","display_name":"Global optimization","level":2,"score":0.2768999934196472},{"id":"https://openalex.org/C29265498","wikidata":"https://www.wikidata.org/wiki/Q7047719","display_name":"Noise measurement","level":3,"score":0.27140000462532043},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.26440000534057617},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.26030001044273376},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.2563000023365021},{"id":"https://openalex.org/C104267543","wikidata":"https://www.wikidata.org/wiki/Q208163","display_name":"Signal processing","level":3,"score":0.2538999915122986},{"id":"https://openalex.org/C2778915421","wikidata":"https://www.wikidata.org/wiki/Q3643177","display_name":"Performance improvement","level":2,"score":0.25369998812675476}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.21725","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.21725","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.21725","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.21725","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Fine-tuning":[0],"large":[1],"language":[2],"models":[3],"(LLMs)":[4],"with":[5],"backpropagation":[6],"achieves":[7,181],"high":[8],"performance":[9,164],"but":[10,61],"incurs":[11],"substantial":[12],"memory":[13,189],"overhead,":[14],"limiting":[15],"scalability":[16],"on":[17,30,65,150],"resource-constrained":[18],"hardware.":[19],"Zeroth-order":[20],"(ZO)":[21],"optimization":[22,74],"provides":[23],"a":[24,57,105,184],"memory-efficient":[25],"alternative":[26],"by":[27,54,175],"relying":[28],"solely":[29],"forward":[31],"passes,":[32],"yet":[33],"it":[34],"typically":[35],"suffers":[36],"from":[37,95],"slow":[38],"or":[39],"unstable":[40],"convergence":[41],"due":[42],"to":[43,103,131,177,183],"high-variance":[44],"gradient":[45,122],"estimates.":[46],"Sparse":[47,86],"ZO":[48,73,97,121,139,170],"updates":[49,140],"partially":[50],"address":[51],"this":[52],"issue":[53],"perturbing":[55],"only":[56,79],"subset":[58],"of":[59,118],"parameters,":[60,68],"their":[62],"effectiveness":[63],"hinges":[64],"selecting":[66,110],"informative":[67],"which":[69,90],"is":[70],"challenging":[71],"in":[72],"because":[75],"each":[76,113],"query":[77],"yields":[78],"scalar":[80,96],"feedback.":[81],"We":[82],"propose":[83],"\\textbf{Adaptive":[84],"Curvature-Guided":[85],"Zeroth-Order":[87],"Optimization":[88],"(CurvZO)},":[89],"tracks":[91],"curvature":[92,134],"signals":[93,102],"online":[94],"feedback":[98],"and":[99,145,152,165,180],"leverages":[100],"these":[101],"construct":[104],"parameter-wise":[106],"sampling":[107],"distribution":[108],"for":[109],"coordinates":[111],"at":[112],"update,":[114],"reducing":[115],"the":[116,119,128,132],"variance":[117],"sparse":[120,138],"estimator.":[123],"Moreover,":[124],"CurvZO":[125,160],"dynamically":[126],"adapts":[127],"perturbation":[129],"budget":[130],"evolving":[133],"signal":[135],"distribution,":[136],"yielding":[137],"that":[141,159],"remain":[142],"both":[143],"focused":[144],"sufficiently":[146],"exploratory.":[147],"Extensive":[148],"experiments":[149],"OPT":[151],"Llama":[153],"across":[154],"diverse":[155],"NLP":[156],"tasks":[157],"show":[158],"consistently":[161],"improves":[162,173],"fine-tuning":[163],"reduces":[166],"training":[167],"time":[168],"over":[169],"baselines.":[171],"It":[172],"accuracy":[174],"up":[176,182],"4.4":[178],"points":[179],"$2\\times$":[185],"speedup,":[186],"while":[187],"preserving":[188],"efficiency.":[190]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-03-25T00:00:00"}