{"id":"https://openalex.org/W7160905945","doi":"https://doi.org/10.48550/arxiv.2605.09291","title":"dFlowGRPO: Rate-Aware Policy Optimization for Discrete Flow Models","display_name":"dFlowGRPO: Rate-Aware Policy Optimization for Discrete Flow Models","publication_year":2026,"publication_date":"2026-05-10","ids":{"openalex":"https://openalex.org/W7160905945","doi":"https://doi.org/10.48550/arxiv.2605.09291"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.09291","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.09291","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.09291","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5124825817","display_name":"Zhengyan Wan","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Wan, Zhengyan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5005136271","display_name":"Yidong Ouyang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ouyang, Yidong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5043898209","display_name":"Panwen Hu","orcid":"https://orcid.org/0000-0001-6183-6598"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hu, Panwen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5135982580","display_name":"Qiang Sun","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sun, Qiang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5124825817"],"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.33149999380111694,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.33149999380111694,"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/T10462","display_name":"Reinforcement Learning in Robotics","score":0.15970000624656677,"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/T10028","display_name":"Topic Modeling","score":0.07370000332593918,"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/reinforcement-learning","display_name":"Reinforcement learning","score":0.632099986076355},{"id":"https://openalex.org/keywords/discrete-time-and-continuous-time","display_name":"Discrete time and continuous time","score":0.5929999947547913},{"id":"https://openalex.org/keywords/discrete-choice","display_name":"Discrete choice","score":0.5515000224113464},{"id":"https://openalex.org/keywords/markov-decision-process","display_name":"Markov decision process","score":0.5364999771118164},{"id":"https://openalex.org/keywords/markov-chain","display_name":"Markov chain","score":0.4794999957084656},{"id":"https://openalex.org/keywords/flow","display_name":"Flow (mathematics)","score":0.4634999930858612},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.398499995470047},{"id":"https://openalex.org/keywords/markov-process","display_name":"Markov process","score":0.3978999853134155},{"id":"https://openalex.org/keywords/conditional-probability","display_name":"Conditional probability","score":0.36579999327659607}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6647999882698059},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.632099986076355},{"id":"https://openalex.org/C55689738","wikidata":"https://www.wikidata.org/wiki/Q15963867","display_name":"Discrete time and continuous time","level":2,"score":0.5929999947547913},{"id":"https://openalex.org/C190669063","wikidata":"https://www.wikidata.org/wiki/Q5282043","display_name":"Discrete choice","level":2,"score":0.5515000224113464},{"id":"https://openalex.org/C106189395","wikidata":"https://www.wikidata.org/wiki/Q176789","display_name":"Markov decision process","level":3,"score":0.5364999771118164},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.4794999957084656},{"id":"https://openalex.org/C38349280","wikidata":"https://www.wikidata.org/wiki/Q1434290","display_name":"Flow (mathematics)","level":2,"score":0.4634999930858612},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.4020000100135803},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4011000096797943},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.398499995470047},{"id":"https://openalex.org/C159886148","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov process","level":2,"score":0.3978999853134155},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.36629998683929443},{"id":"https://openalex.org/C44492722","wikidata":"https://www.wikidata.org/wiki/Q327069","display_name":"Conditional probability","level":2,"score":0.36579999327659607},{"id":"https://openalex.org/C2777735758","wikidata":"https://www.wikidata.org/wiki/Q817765","display_name":"Path (computing)","level":2,"score":0.36070001125335693},{"id":"https://openalex.org/C145671259","wikidata":"https://www.wikidata.org/wiki/Q1493786","display_name":"Discrete optimization","level":3,"score":0.359499990940094},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.3481000065803528},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.3476000130176544},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.32269999384880066},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.32109999656677246},{"id":"https://openalex.org/C163836022","wikidata":"https://www.wikidata.org/wiki/Q6771326","display_name":"Markov model","level":3,"score":0.2904999852180481},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.2824000120162964},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.2775000035762787},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.265500009059906},{"id":"https://openalex.org/C196340769","wikidata":"https://www.wikidata.org/wiki/Q7698910","display_name":"Temporal difference learning","level":3,"score":0.2639999985694885},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.26339998841285706},{"id":"https://openalex.org/C22171661","wikidata":"https://www.wikidata.org/wiki/Q1074380","display_name":"Stochastic game","level":2,"score":0.26170000433921814}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.09291","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.09291","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.09291","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.09291","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","score":0.6884679198265076,"id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Discrete":[0],"flow":[1,56,75,133],"models":[2,10,19,57,76,168],"(DFMs)":[3],"are":[4],"a":[5,25,29,36,68,79,101,129],"class":[6],"of":[7,32,82],"flexible":[8],"generative":[9],"for":[11,73,95,155],"generating":[12],"discrete":[13,55,65,74,132],"data,":[14],"and":[15,35,85,97,117,135,142,161],"diffusion":[16],"large":[17],"language":[18],"(dLLMs)":[20],"can":[21],"be":[22],"viewed":[23],"as":[24,100],"special":[26],"case":[27],"with":[28,165],"specific":[30],"choice":[31],"mixture":[33],"path":[34],"masked":[37],"source":[38,87],"distribution.":[39],"While":[40],"several":[41],"recent":[42,130],"works":[43],"have":[44],"explored":[45],"reinforcement":[46,70,122],"learning":[47,71],"into":[48],"dLLMs,":[49],"its":[50],"application":[51],"to":[52,107,127],"more":[53],"general":[54],"remains":[58],"underexplored.":[59],"In":[60],"this":[61],"work,":[62],"we":[63],"present":[64],"Flow-GRPO":[66],"(dFlowGRPO),":[67],"unified":[69],"framework":[72],"that":[77,149],"supports":[78],"broad":[80],"family":[81],"probability":[83,94],"paths":[84],"non-masked":[86],"distributions.":[88],"We":[89,124],"derive":[90],"the":[91,112,118],"full":[92],"trajectory":[93],"DFMs":[96],"formulate":[98],"denoising":[99],"Markov":[102],"decision":[103],"process,":[104],"enabling":[105],"dFlowGRPO":[106,126,150],"incorporate":[108],"information":[109],"from":[110],"both":[111,139],"associated":[113],"conditional":[114],"transition":[115],"rates":[116],"posterior":[119],"model":[120],"during":[121],"learning.":[123],"apply":[125],"FUDOKI,":[128],"multimodal":[131,143],"model,":[134],"evaluate":[136],"it":[137],"on":[138,157,177],"image":[140],"generation":[141,159],"understanding":[144,178],"tasks.":[145,179],"Empirical":[146],"results":[147],"show":[148],"outperforms":[151],"existing":[152],"GRPO-type":[153],"methods":[154],"dLLMs":[156],"text-to-image":[158],"tasks":[160],"achieves":[162],"performance":[163],"competitive":[164],"continuous":[166],"flow-based":[167],"trained":[169],"using":[170],"FlowGRPO,":[171],"while":[172],"also":[173],"demonstrating":[174],"strong":[175],"capabilities":[176]},"counts_by_year":[],"updated_date":"2026-05-13T06:11:35.469786","created_date":"2026-05-13T00:00:00"}
