{"id":"https://openalex.org/W7158169012","doi":"https://doi.org/10.48550/arxiv.2604.24953","title":"ViPO: Visual Preference Optimization at Scale","display_name":"ViPO: Visual Preference Optimization at Scale","publication_year":2026,"publication_date":"2026-04-27","ids":{"openalex":"https://openalex.org/W7158169012","doi":"https://doi.org/10.48550/arxiv.2604.24953"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.24953","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.24953","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.2604.24953","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5134826687","display_name":"Ming Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Ming","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134850404","display_name":"Jie Wu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wu, Jie","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134833265","display_name":"Justin Cui","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cui, Justin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134865120","display_name":"Xiaojie Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Xiaojie","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134847662","display_name":"Rui Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Rui","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5134825310","display_name":"Chen Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Chen","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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.39570000767707825,"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"}},"topics":[{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.39570000767707825,"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"}},{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","score":0.2117999941110611,"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"}},{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.04149999842047691,"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/preference","display_name":"Preference","score":0.6180999875068665},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.5745999813079834},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.42800000309944153},{"id":"https://openalex.org/keywords/adaptability","display_name":"Adaptability","score":0.39329999685287476},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.37700000405311584},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.3693999946117401},{"id":"https://openalex.org/keywords/convergence","display_name":"Convergence (economics)","score":0.36629998683929443},{"id":"https://openalex.org/keywords/preference-learning","display_name":"Preference learning","score":0.34929999709129333}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7016000151634216},{"id":"https://openalex.org/C2781249084","wikidata":"https://www.wikidata.org/wiki/Q908656","display_name":"Preference","level":2,"score":0.6180999875068665},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5928000211715698},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5753999948501587},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5745999813079834},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.42800000309944153},{"id":"https://openalex.org/C177606310","wikidata":"https://www.wikidata.org/wiki/Q5674297","display_name":"Adaptability","level":2,"score":0.39329999685287476},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.37700000405311584},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.3693999946117401},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.36629998683929443},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3546999990940094},{"id":"https://openalex.org/C181204326","wikidata":"https://www.wikidata.org/wiki/Q7239820","display_name":"Preference learning","level":3,"score":0.34929999709129333},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.3269999921321869},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.32670000195503235},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.3172000050544739},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.2904999852180481},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.29019999504089355},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.26669999957084656},{"id":"https://openalex.org/C2777868144","wikidata":"https://www.wikidata.org/wiki/Q7239817","display_name":"Preference elicitation","level":3,"score":0.2651999890804291},{"id":"https://openalex.org/C24756922","wikidata":"https://www.wikidata.org/wiki/Q1757694","display_name":"Data quality","level":3,"score":0.26100000739097595},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.25679999589920044},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.2554999887943268},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.2547000050544739}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.24953","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.24953","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.2604.24953","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.24953","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":{"While":[0],"preference":[1,21,25,103,114,142,226,244],"optimization":[2,104,172],"is":[3,239],"crucial":[4],"for":[5,182,209,241],"improving":[6],"visual":[7,102,190,243],"generative":[8,135],"models,":[9],"how":[10],"to":[11,44,151,159],"effectively":[12],"scale":[13],"this":[14],"paradigm":[15],"remains":[16,180],"largely":[17],"unexplored.":[18],"Current":[19],"open-source":[20,225],"datasets":[22,42,88,195],"contain":[23],"conflicting":[24],"patterns,":[26,86],"where":[27],"winners":[28],"excel":[29],"in":[30,35],"some":[31],"dimensions":[32],"but":[33],"underperform":[34],"others.":[36],"Naively":[37],"optimizing":[38],"on":[39,74,207,223],"such":[40],"noisy":[41,194],"fails":[43],"learn":[45],"preferences,":[46],"hindering":[47],"effective":[48,78],"scaling.":[49],"To":[50,99],"enhance":[51],"robustness":[52],"against":[53],"noise,":[54],"we":[55,109],"propose":[56],"Poly-DPO,":[57],"which":[58],"extends":[59],"the":[60,155],"DPO":[61],"objective":[62],"with":[63,116,144,175],"an":[64],"additional":[65],"polynomial":[66],"term":[67],"that":[68,231],"dynamically":[69],"adjusts":[70],"model":[71],"confidence":[72],"based":[73],"dataset":[75,115,165],"characteristics,":[76],"enabling":[77],"learning":[79],"across":[80,122,131,189],"diverse":[81,138],"data":[82,107,177,237],"distributions.":[83,98,146],"Beyond":[84],"biased":[85],"existing":[87,224],"suffer":[89],"from":[90],"low":[91],"resolution,":[92],"limited":[93],"prompt":[94],"diversity,":[95],"and":[96,125,137,167,202,211,236],"imbalanced":[97],"facilitate":[100],"large-scale":[101],"by":[105],"tackling":[106],"bottlenecks,":[108],"construct":[110],"ViPO,":[111,215],"a":[112],"massive-scale":[113],"1M":[117],"image":[118],"pairs":[119,128],"at":[120,129],"1024px":[121],"five":[123],"categories":[124],"300K":[126],"video":[127],"720p+":[130],"three":[132],"categories.":[133],"State-of-the-art":[134],"models":[136,216],"prompts":[139],"ensure":[140],"reliable":[141],"signals":[143],"balanced":[145],"Remarkably,":[147],"when":[148],"applying":[149],"Poly-DPO":[150,199],"our":[152,187],"high-quality":[153],"dataset,":[154],"optimal":[156],"configuration":[157],"converges":[158],"standard":[160],"DPO.":[161],"This":[162],"convergence":[163],"validates":[164],"quality":[166,238],"Poly-DPO's":[168],"adaptive":[169],"nature:":[170],"sophisticated":[171],"becomes":[173],"unnecessary":[174],"sufficient":[176],"quality,":[178],"yet":[179],"valuable":[181],"imperfect":[183],"datasets.":[184,227],"We":[185],"validate":[186],"approach":[188],"generation":[191],"models.":[192],"On":[193],"like":[196],"Pick-a-Pic":[197],"V2,":[198],"achieves":[200],"6.87":[201],"2.32":[203],"gains":[204],"over":[205],"Diffusion-DPO":[206],"GenEval":[208],"SD1.5":[210],"SDXL,":[212],"respectively.":[213],"For":[214],"achieve":[217],"performance":[218],"far":[219],"exceeding":[220],"those":[221],"trained":[222],"These":[228],"results":[229],"confirm":[230],"addressing":[232],"both":[233],"algorithmic":[234],"adaptability":[235],"essential":[240],"scaling":[242],"optimization.":[245]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-04-30T00:00:00"}