{"id":"https://openalex.org/W7155208493","doi":"https://doi.org/10.48550/arxiv.2604.19009","title":"Guiding Distribution Matching Distillation with Gradient-Based Reinforcement Learning","display_name":"Guiding Distribution Matching Distillation with Gradient-Based Reinforcement Learning","publication_year":2026,"publication_date":"2026-04-21","ids":{"openalex":"https://openalex.org/W7155208493","doi":"https://doi.org/10.48550/arxiv.2604.19009"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.19009","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.19009","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":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2604.19009","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5114099654","display_name":"Linwei Dong","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dong, Linwei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134217766","display_name":"Ruoyu Guo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Guo, Ruoyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068137955","display_name":"Ge Bai","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bai, Ge","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134360999","display_name":"Zehuan Yuan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yuan, Zehuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134265925","display_name":"Yawei Luo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Luo, Yawei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5134282607","display_name":"Changqing Zou","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zou, Changqing","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":0.3847000002861023,"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":0.3847000002861023,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.18469999730587006,"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/T10653","display_name":"Robot Manipulation and Learning","score":0.05040000006556511,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/distillation","display_name":"Distillation","score":0.7511000037193298},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.6624000072479248},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.6363000273704529},{"id":"https://openalex.org/keywords/weighting","display_name":"Weighting","score":0.6184999942779541},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.5192000269889832},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.49140000343322754},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.45249998569488525},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.43779999017715454}],"concepts":[{"id":"https://openalex.org/C204030448","wikidata":"https://www.wikidata.org/wiki/Q101017","display_name":"Distillation","level":2,"score":0.7511000037193298},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7289999723434448},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.6624000072479248},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.6363000273704529},{"id":"https://openalex.org/C183115368","wikidata":"https://www.wikidata.org/wiki/Q856577","display_name":"Weighting","level":2,"score":0.6184999942779541},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5249000191688538},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.5192000269889832},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.49140000343322754},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.45249998569488525},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4498000144958496},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.43779999017715454},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.42289999127388},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.36820000410079956},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.3264000117778778},{"id":"https://openalex.org/C154030694","wikidata":"https://www.wikidata.org/wiki/Q1436074","display_name":"Fractionating column","level":3,"score":0.3255000114440918},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.32440000772476196},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.29260000586509705},{"id":"https://openalex.org/C111696304","wikidata":"https://www.wikidata.org/wiki/Q2303697","display_name":"Sorting","level":2,"score":0.2922999858856201},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.29120001196861267},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.26159998774528503},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2599000036716461},{"id":"https://openalex.org/C110121322","wikidata":"https://www.wikidata.org/wiki/Q865811","display_name":"Distribution (mathematics)","level":2,"score":0.25940001010894775},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.2567000091075897}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.19009","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.19009","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":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.19009","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.19009","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":"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":{"Diffusion":[0],"distillation,":[1],"exemplified":[2],"by":[3,81],"Distribution":[4],"Matching":[5],"Distillation":[6],"(DMD),":[7],"has":[8],"shown":[9],"great":[10],"promise":[11],"in":[12,168],"few-step":[13,149],"generation":[14],"but":[15],"often":[16],"sacrifices":[17],"quality":[18,114,157],"for":[19,93,148],"sampling":[20],"speed.":[21],"While":[22],"integrating":[23],"Reinforcement":[24],"Learning":[25],"(RL)":[26],"into":[27],"distillation":[28,52,83,116,133],"offers":[29],"potential,":[30],"a":[31,73,145],"naive":[32],"fusion":[33],"of":[34,63,115,158],"these":[35,68],"two":[36],"objectives":[37],"relies":[38],"on":[39],"suboptimal":[40],"raw":[41,86],"sample":[42],"evaluation.":[43],"This":[44,118],"sample-based":[45],"scoring":[46],"creates":[47],"inherent":[48],"conflicts":[49],"with":[50,131],"the":[51,60,78,90,97,113,128,132,156],"trajectory":[53],"and":[54,162,170],"produces":[55],"unreliable":[56],"rewards":[57],"due":[58],"to":[59,110],"noisy":[61],"nature":[62],"early-stage":[64],"generation.":[65,150],"To":[66],"overcome":[67],"limitations,":[69],"we":[70],"propose":[71],"GDMD,":[72],"novel":[74],"framework":[75,105],"that":[76,126,142],"redefines":[77],"reward":[79,108],"mechanism":[80],"prioritizing":[82],"gradients":[84,99],"over":[85],"pixel":[87],"outputs":[88],"as":[89,100,122],"primary":[91],"signal":[92],"optimization.":[94],"By":[95],"reinterpreting":[96],"DMD":[98],"implicit":[101],"target":[102],"tensors,":[103],"our":[104,152],"enables":[106],"existing":[107],"models":[109,154],"directly":[111],"evaluate":[112],"updates.":[117],"gradient-level":[119],"guidance":[120],"functions":[121],"an":[123],"adaptive":[124],"weighting":[125],"synchronizes":[127],"RL":[129],"policy":[130],"objective,":[134],"effectively":[135],"neutralizing":[136],"optimization":[137],"divergence.":[138],"Empirical":[139],"results":[140,167],"show":[141],"GDMD":[143],"sets":[144],"new":[146],"SOTA":[147],"Specifically,":[151],"4-step":[153],"outperform":[155],"their":[159],"multi-step":[160],"teacher":[161],"substantially":[163],"exceed":[164],"previous":[165],"DMDR":[166],"GenEval":[169],"human-preference":[171],"metrics,":[172],"exhibiting":[173],"strong":[174],"scalability":[175],"potential.":[176]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-23T00:00:00"}