{"id":"https://openalex.org/W7169544084","doi":"https://doi.org/10.48550/arxiv.2607.15273","title":"MeanFlowNFT: Bringing Forward-Process RL to Average-Velocity Generators","display_name":"MeanFlowNFT: Bringing Forward-Process RL to Average-Velocity Generators","publication_year":2026,"publication_date":"2026-07-16","ids":{"openalex":"https://openalex.org/W7169544084","doi":"https://doi.org/10.48550/arxiv.2607.15273"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2607.15273","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.15273","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.2607.15273","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5141080428","display_name":"Yushi Huang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Huang, Yushi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113903386","display_name":"X J Zhou","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhou, Xiangxin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5141077676","display_name":"Jun Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Jun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5141111489","display_name":"Liefeng Bo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bo, Liefeng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5141053880","display_name":"Tianyu Pang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pang, Tianyu","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/T10462","display_name":"Reinforcement Learning in Robotics","score":0.14589999616146088,"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/T10462","display_name":"Reinforcement Learning in Robotics","score":0.14589999616146088,"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":0.12800000607967377,"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":0.10260000079870224,"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/sampling","display_name":"Sampling (signal processing)","score":0.5968000292778015},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.5961999893188477},{"id":"https://openalex.org/keywords/generator","display_name":"Generator (circuit theory)","score":0.4812999963760376},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.4577000141143799},{"id":"https://openalex.org/keywords/importance-sampling","display_name":"Importance sampling","score":0.3790000081062317},{"id":"https://openalex.org/keywords/bridge","display_name":"Bridge (graph theory)","score":0.3562000095844269}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6863999962806702},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.5968000292778015},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.5961999893188477},{"id":"https://openalex.org/C2780992000","wikidata":"https://www.wikidata.org/wiki/Q17016113","display_name":"Generator (circuit theory)","level":3,"score":0.4812999963760376},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.46970000863075256},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.4577000141143799},{"id":"https://openalex.org/C52740198","wikidata":"https://www.wikidata.org/wiki/Q1539564","display_name":"Importance sampling","level":3,"score":0.3790000081062317},{"id":"https://openalex.org/C100776233","wikidata":"https://www.wikidata.org/wiki/Q2532492","display_name":"Bridge (graph theory)","level":2,"score":0.3562000095844269},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3379000127315521},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3334999978542328},{"id":"https://openalex.org/C69357855","wikidata":"https://www.wikidata.org/wiki/Q163214","display_name":"Diffusion","level":2,"score":0.3296999931335449},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3034999966621399},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.30300000309944153},{"id":"https://openalex.org/C2781395549","wikidata":"https://www.wikidata.org/wiki/Q4680762","display_name":"Adaptive sampling","level":3,"score":0.2919999957084656},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.28189998865127563},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.27730000019073486},{"id":"https://openalex.org/C38349280","wikidata":"https://www.wikidata.org/wiki/Q1434290","display_name":"Flow (mathematics)","level":2,"score":0.2757999897003174}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2607.15273","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.15273","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.2607.15273","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.15273","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":{"MeanFlow":[0,63,71,86],"generators":[1,155],"achieve":[2],"fast":[3,123],"few-step":[4,124,154],"sampling":[5,176],"by":[6,84],"predicting":[7],"average":[8,74,90,119],"velocities":[9],"over":[10],"time":[11],"intervals,":[12],"making":[13,108],"them":[14],"attractive":[15],"for":[16,112],"efficient":[17,44],"generation.":[18,125],"Reinforcement":[19],"learning":[20],"(RL)":[21],"has":[22],"become":[23],"a":[24,174,186],"powerful":[25],"way":[26],"to":[27,62,105],"align":[28],"diffusion":[29,170],"and":[30,36,91,139,164],"flow":[31],"models":[32],"with":[33,73],"human":[34],"preferences":[35],"task-specific":[37],"objectives.":[38],"In":[39],"particular,":[40],"DiffusionNFT":[41,66,103],"offers":[42],"an":[43,96],"forward-process":[45],"RL":[46,60,194],"framework":[47],"that":[48,129,143],"does":[49],"not":[50],"require":[51],"reverse-process":[52],"trajectories":[53],"or":[54],"likelihood":[55],"estimation.":[56],"However,":[57],"applying":[58],"such":[59],"methods":[61],"remains":[64,115],"underexplored.":[65],"optimizes":[67],"instantaneous":[68,92],"velocities,":[69,93],"whereas":[70],"samples":[72],"velocities.":[75],"To":[76],"bridge":[77],"this":[78,106],"gap,":[79],"we":[80,94],"introduce":[81],"MeanFlowNFT.":[82],"Inspired":[83],"the":[85,102,118],"identity,":[87],"which":[88],"bridges":[89],"construct":[95],"induced":[97],"instantaneous-velocity":[98],"predictor.":[99],"We":[100,126],"apply":[101],"objective":[104],"predictor,":[107],"reward":[109],"optimization":[110],"well-defined":[111],"MeanFlow.":[113],"Sampling":[114],"based":[116],"on":[117,137,156,162,180],"velocity,":[120],"preserving":[121],"MeanFlow's":[122],"further":[127],"prove":[128],"MeanFlowNFT":[130,144,184],"inherits":[131],"DiffusionNFT's":[132],"strict":[133],"policy-improvement":[134],"guarantee.":[135],"Experiments":[136],"image":[138],"video":[140],"generation":[141],"show":[142],"consistently":[145],"improves":[146],"baselines.":[147],"Moreover,":[148],"it":[149],"outperforms":[150],"prior":[151],"state-of-the-art":[152],"RL-tuned":[153,169],"most":[157],"metrics":[158],"($6$":[159],"of":[160,189],"$8$":[161],"SD3.5-M),":[163],"can":[165],"even":[166],"surpass":[167],"multi-step":[168],"while":[171],"using":[172],"only":[173],"few":[175],"steps.":[177],"For":[178],"instance,":[179],"Wan":[181],"2.1,":[182],"$4$-step":[183],"reaches":[185],"VBench":[187],"score":[188],"$84.33$,":[190],"surpassing":[191],"$50$-step":[192],"LongCat-Video":[193],"($82.57$).":[195]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-07-18T00:00:00"}