{"id":"https://openalex.org/W7160945880","doi":"https://doi.org/10.48550/arxiv.2605.09433","title":"Offline Preference Optimization for Rectified Flow with Noise-Tracked Pairs","display_name":"Offline Preference Optimization for Rectified Flow with Noise-Tracked Pairs","publication_year":2026,"publication_date":"2026-05-10","ids":{"openalex":"https://openalex.org/W7160945880","doi":"https://doi.org/10.48550/arxiv.2605.09433"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.09433","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.09433","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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.2605.09433","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5135956793","display_name":"Yunhong Lu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lu, Yunhong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135945753","display_name":"Qichao Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Qichao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135965601","display_name":"Hengyuan Cao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cao, Hengyuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135967778","display_name":"Xiaoyin Xu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xu, Xiaoyin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5135991992","display_name":"Min Zhang","orcid":"https://orcid.org/0000-0002-6771-007X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Min","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":0.1615000069141388,"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":0.1615000069141388,"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/T10848","display_name":"Advanced Multi-Objective Optimization Algorithms","score":0.10620000213384628,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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/T10462","display_name":"Reinforcement Learning in Robotics","score":0.0885000005364418,"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/regularization","display_name":"Regularization (linguistics)","score":0.6546000242233276},{"id":"https://openalex.org/keywords/preference","display_name":"Preference","score":0.5902000069618225},{"id":"https://openalex.org/keywords/stability","display_name":"Stability (learning theory)","score":0.4643999934196472},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.4505000114440918},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.43070000410079956},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.42750000953674316},{"id":"https://openalex.org/keywords/property","display_name":"Property (philosophy)","score":0.4180000126361847}],"concepts":[{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.6546000242233276},{"id":"https://openalex.org/C2781249084","wikidata":"https://www.wikidata.org/wiki/Q908656","display_name":"Preference","level":2,"score":0.5902000069618225},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.47510001063346863},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4724000096321106},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.4643999934196472},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4505000114440918},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.43070000410079956},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.42750000953674316},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4187000095844269},{"id":"https://openalex.org/C189950617","wikidata":"https://www.wikidata.org/wiki/Q937228","display_name":"Property (philosophy)","level":2,"score":0.4180000126361847},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.3937999904155731},{"id":"https://openalex.org/C147764199","wikidata":"https://www.wikidata.org/wiki/Q6865248","display_name":"Minification","level":2,"score":0.3824999928474426},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.38119998574256897},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.38109999895095825},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3781000077724457},{"id":"https://openalex.org/C38349280","wikidata":"https://www.wikidata.org/wiki/Q1434290","display_name":"Flow (mathematics)","level":2,"score":0.3662000000476837},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.3474999964237213},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.28110000491142273},{"id":"https://openalex.org/C2778572836","wikidata":"https://www.wikidata.org/wiki/Q380933","display_name":"Space (punctuation)","level":2,"score":0.27799999713897705},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2680000066757202}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.09433","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.09433","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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.2605.09433","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.09433","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":{"Existing":[0],"preference":[1,87,137,182],"datasets":[2],"for":[3,17,45,82,136],"text-to-image":[4],"models":[5,47],"typically":[6],"store":[7],"only":[8],"the":[9,62,91,102,112,126,149,155],"final":[10],"winner/loser":[11,99],"images.":[12],"This":[13],"representation":[14],"is":[15,24],"insufficient":[16],"rectified":[18,83],"flow":[19],"(RF)":[20],"models,":[21],"whose":[22],"generation":[23],"naturally":[25],"indexed":[26],"by":[27,89],"a":[28,35,109,132,143],"specific":[29],"prior":[30,42,93],"noise":[31],"sample":[32,169],"and":[33,66,130,160,162,168],"follows":[34],"nearly":[36],"straight":[37],"denoising":[38],"trajectory.":[39],"In":[40,139],"contrast,":[41],"DPO-style":[43],"alignment":[44,79],"diffusion":[46],"commonly":[48],"estimates":[49],"trajectories":[50],"using":[51],"an":[52,77],"independent":[53],"forward":[54],"noising":[55],"process,":[56],"which":[57,124],"can":[58],"be":[59],"mismatched":[60],"to":[61,96],"true":[63],"reverse":[64],"dynamics":[65],"introduces":[67],"unnecessary":[68],"variance.":[69],"We":[70],"propose":[71],"Prior":[72],"Noise-Aware":[73],"Preference":[74],"Optimization":[75],"(PNAPO),":[76],"off-policy":[78],"framework":[80],"specialized":[81],"flow.":[84],"PNAPO":[85,179],"augments":[86],"data":[88],"retaining":[90],"paired":[92],"noises":[94],"used":[95],"generate":[97],"each":[98],"image,":[100],"turning":[101],"standard":[103],"(prompt,":[104],"winner,":[105],"loser)":[106],"triplet":[107],"into":[108],"sextuple.":[110],"Leveraging":[111],"straight-line":[113],"property":[114],"of":[115],"RF,":[116],"we":[117,141],"estimate":[118],"intermediate":[119],"states":[120],"via":[121],"noise-image":[122],"interpolation,":[123],"constrains":[125],"trajectory":[127],"estimation":[128],"space":[129],"yields":[131],"tighter":[133],"surrogate":[134],"objective":[135],"optimization.":[138],"addition,":[140],"introduce":[142],"dynamic":[144],"regularization":[145,151],"strategy":[146],"that":[147,178],"adapts":[148],"DPO":[150],"based":[152],"on":[153,172],"(i)":[154],"reward":[156],"gap":[157],"between":[158],"winner":[159],"loser":[161],"(ii)":[163],"training":[164,187],"progress,":[165],"improving":[166],"stability":[167],"efficiency.":[170],"Experiments":[171],"state-of-the-art":[173],"RF":[174],"T2I":[175],"backbones":[176],"show":[177],"consistently":[180],"improves":[181],"metrics":[183],"while":[184],"substantially":[185],"reducing":[186],"compute.":[188]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-13T00:00:00"}
