{"id":"https://openalex.org/W7165635822","doi":"https://doi.org/10.48550/arxiv.2606.22630","title":"Scalable Maximum Entropy Reinforcement Learning for Diffusion Policies via Adjoint Matching","display_name":"Scalable Maximum Entropy Reinforcement Learning for Diffusion Policies via Adjoint Matching","publication_year":2026,"publication_date":"2026-06-21","ids":{"openalex":"https://openalex.org/W7165635822","doi":"https://doi.org/10.48550/arxiv.2606.22630"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.22630","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.22630","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.2606.22630","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5093774977","display_name":"Serge Thilges","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Thilges, Serge","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139154488","display_name":"Onur Celik","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Celik, Onur","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5012897974","display_name":"Denis Blessing","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Blessing, Denis","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5114637377","display_name":"Emiliyan Gospodinov","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gospodinov, Emiliyan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5139180291","display_name":"Gerhard Neumann","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Neumann, Gerhard","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":0.8815000057220459,"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":0.8815000057220459,"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/T12794","display_name":"Adaptive Dynamic Programming Control","score":0.04340000078082085,"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/T12101","display_name":"Advanced Bandit Algorithms Research","score":0.008299999870359898,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.7105000019073486},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.6467999815940857},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.5871000289916992},{"id":"https://openalex.org/keywords/stability","display_name":"Stability (learning theory)","score":0.5110999941825867},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.4327999949455261},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.4117000102996826},{"id":"https://openalex.org/keywords/principle-of-maximum-entropy","display_name":"Principle of maximum entropy","score":0.36039999127388},{"id":"https://openalex.org/keywords/diffusion","display_name":"Diffusion","score":0.3434999883174896}],"concepts":[{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.7105000019073486},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7038999795913696},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.6467999815940857},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5871000289916992},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.5220000147819519},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.5110999941825867},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.4327999949455261},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.4117000102996826},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.38339999318122864},{"id":"https://openalex.org/C9679016","wikidata":"https://www.wikidata.org/wiki/Q1417473","display_name":"Principle of maximum entropy","level":2,"score":0.36039999127388},{"id":"https://openalex.org/C69357855","wikidata":"https://www.wikidata.org/wiki/Q163214","display_name":"Diffusion","level":2,"score":0.3434999883174896},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.34130001068115234},{"id":"https://openalex.org/C91575142","wikidata":"https://www.wikidata.org/wiki/Q1971426","display_name":"Optimal control","level":2,"score":0.33219999074935913},{"id":"https://openalex.org/C106189395","wikidata":"https://www.wikidata.org/wiki/Q176789","display_name":"Markov decision process","level":3,"score":0.33079999685287476},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3089999854564667},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.2973000109195709},{"id":"https://openalex.org/C3018263672","wikidata":"https://www.wikidata.org/wiki/Q1296251","display_name":"Efficient algorithm","level":2,"score":0.2921000123023987},{"id":"https://openalex.org/C159886148","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov process","level":2,"score":0.2849999964237213},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2743000090122223},{"id":"https://openalex.org/C8272713","wikidata":"https://www.wikidata.org/wiki/Q176737","display_name":"Stochastic process","level":2,"score":0.2653000056743622},{"id":"https://openalex.org/C55479107","wikidata":"https://www.wikidata.org/wiki/Q97663916","display_name":"Stochastic approximation","level":3,"score":0.25099998712539673}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.22630","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.22630","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.2606.22630","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.22630","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":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Diffusion":[0],"policies":[1,62,130],"have":[2],"recently":[3],"emerged":[4],"as":[5,43],"a":[6,55],"powerful":[7],"paradigm":[8],"for":[9,59,86,133],"representing":[10],"complex":[11],"action":[12],"distributions":[13],"in":[14,32,67,111],"reinforcement":[15],"learning":[16],"(RL).":[17],"However,":[18],"their":[19],"application":[20],"to":[21],"online":[22,134],"RL":[23,135],"remains":[24],"limited":[25],"by":[26,63],"the":[27,33,84,94,104,109],"challenge":[28],"of":[29,35,108],"scalable":[30],"training":[31,81],"absence":[34],"ground-truth":[36],"data,":[37],"where":[38],"standard":[39],"optimization":[40],"techniques":[41],"such":[42],"score":[44],"matching":[45],"are":[46],"not":[47],"directly":[48],"applicable.":[49],"In":[50],"this":[51],"work,":[52],"we":[53,98],"introduce":[54],"highly":[56],"efficient":[57],"algorithm":[58],"optimizing":[60],"diffusion":[61,95,129],"leveraging":[64],"recent":[65],"advances":[66],"stochastic":[68],"optimal":[69],"control.":[70],"Our":[71],"approach":[72,119],"is":[73],"based":[74],"on":[75],"adjoint":[76],"matching,":[77],"which":[78],"enables":[79],"simulation-free":[80],"and":[82,106],"circumvents":[83],"need":[85],"explicit":[87],"likelihood":[88],"estimation":[89],"or":[90],"costly":[91],"backpropagation":[92],"through":[93],"process.":[96],"Furthermore,":[97],"propose":[99],"several":[100],"extensions":[101],"that":[102,117],"improve":[103],"robustness":[105],"stability":[107],"method":[110],"practical":[112],"settings.":[113],"Empirical":[114],"results":[115],"demonstrate":[116],"our":[118],"achieves":[120],"competitive":[121],"performance":[122],"while":[123],"significantly":[124],"reducing":[125],"computational":[126],"overhead,":[127],"making":[128],"more":[131],"viable":[132],"scenarios.":[136]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-06-24T00:00:00"}