{"id":"https://openalex.org/W7140161742","doi":"https://doi.org/10.48550/arxiv.2603.21621","title":"Path-Space Mirror Descent for On-Policy Reinforcement Learning under the Generalized Schr\u00f6dinger Bridge","display_name":"Path-Space Mirror Descent for On-Policy Reinforcement Learning under the Generalized Schr\u00f6dinger Bridge","publication_year":2026,"publication_date":"2026-03-23","ids":{"openalex":"https://openalex.org/W7140161742","doi":"https://doi.org/10.48550/arxiv.2603.21621"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.21621","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.21621","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.2603.21621","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Gong, Yuehu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gong, Yuehu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Wang, Zeyuan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Zeyuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Chen, Yulin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Yulin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Ding, Shutong","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ding, Shutong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Zhou, Qingyuan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhou, Qingyuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":null,"display_name":"Fu, Yanwei","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fu, Yanwei","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":0.33869999647140503,"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":0.33869999647140503,"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":0.18359999358654022,"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":0.058800000697374344,"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/generative-grammar","display_name":"Generative grammar","score":0.7831000089645386},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.5871999859809875},{"id":"https://openalex.org/keywords/stability","display_name":"Stability (learning theory)","score":0.5},{"id":"https://openalex.org/keywords/path","display_name":"Path (computing)","score":0.4828999936580658},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.4812000095844269},{"id":"https://openalex.org/keywords/bridge","display_name":"Bridge (graph theory)","score":0.47540000081062317},{"id":"https://openalex.org/keywords/perspective","display_name":"Perspective (graphical)","score":0.43290001153945923}],"concepts":[{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.7831000089645386},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6022999882698059},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.5871999859809875},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.5},{"id":"https://openalex.org/C2777735758","wikidata":"https://www.wikidata.org/wiki/Q817765","display_name":"Path (computing)","level":2,"score":0.4828999936580658},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.4812000095844269},{"id":"https://openalex.org/C100776233","wikidata":"https://www.wikidata.org/wiki/Q2532492","display_name":"Bridge (graph theory)","level":2,"score":0.47540000081062317},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.43290001153945923},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.43209999799728394},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.4223000109195709},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.40220001339912415},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.39750000834465027},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.3564000129699707},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.3409999907016754},{"id":"https://openalex.org/C174348530","wikidata":"https://www.wikidata.org/wiki/Q188635","display_name":"Bridging (networking)","level":2,"score":0.3301999866962433},{"id":"https://openalex.org/C2776848632","wikidata":"https://www.wikidata.org/wiki/Q853463","display_name":"Clipping (morphology)","level":2,"score":0.30070000886917114},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.29840001463890076},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2533999979496002}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.21621","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.21621","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.2603.21621","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.21621","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":[{"display_name":"Peace, Justice and strong institutions","score":0.7744742631912231,"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":{"Classical":[0],"on-policy":[1,71,90],"algorithms":[2],"such":[3],"as":[4,94,172],"PPO":[5],"and":[6,42,104,160,168],"mirror":[7,111],"descent":[8,112],"policy":[9,14,92,113],"optimization":[10,93],"provide":[11,45],"stable":[12],"proximal":[13,72,129,175],"updates":[15],"through":[16,110],"tractable":[17],"action":[18,48,61,137,145,150],"likelihoods,":[19],"but":[20,50],"are":[21],"typically":[22],"instantiated":[23],"with":[24,69],"simple":[25],"Gaussian":[26],"policies":[27,38],"whose":[28,59],"expressiveness":[29],"can":[30],"be":[31],"limited":[32],"in":[33,131],"complex":[34],"continuous-control":[35,156],"tasks.":[36],"Generative":[37],"based":[39],"on":[40,154],"diffusion":[41],"flow":[43],"models":[44],"more":[46],"expressive":[47],"distributions,":[49],"they":[51],"naturally":[52],"define":[53],"distributions":[54],"over":[55,100],"multi-step":[56,178],"denoising":[57],"paths":[58,103],"terminal":[60,136,149],"density":[62],"is":[63,118],"often":[64],"intractable,":[65],"creating":[66],"a":[67,95,173],"mismatch":[68],"likelihood-based":[70],"updates.":[73],"To":[74],"address":[75],"this":[76],"mismatch,":[77],"we":[78],"introduce":[79],"\\textbf{GSB-MDPO}":[80],"(\\emph{Generalized":[81],"Schr\u00f6dinger":[82,97],"Bridge":[83,98],"Mirror":[84],"Descent":[85],"Policy":[86],"Optimization}),":[87],"which":[88],"formulates":[89],"generative":[91,179],"Generalized":[96],"problem":[99],"state-conditioned":[101],"generation":[102],"instantiates":[105],"the":[106,120,125,128,135,143,163],"resulting":[107],"path-measure":[108],"update":[109,176],"optimization.":[114],"The":[115],"key":[116],"insight":[117],"that":[119],"GSB":[121],"path-space":[122,170],"KL":[123],"plays":[124],"role":[126],"of":[127,142,166],"term":[130],"MDPO":[132],"while":[133],"upper-bounding":[134],"KL,":[138],"enabling":[139],"direct":[140],"control":[141],"executed":[144],"distribution":[146],"without":[147],"explicit":[148],"likelihood":[151],"evaluation.":[152],"Experiments":[153],"14":[155],"tasks":[157],"across":[158],"Playground":[159],"Gym-MuJoCo":[161],"demonstrate":[162],"empirical":[164],"effectiveness":[165],"GSB-MDPO":[167],"support":[169],"regularization":[171],"principled":[174],"for":[177],"policies.":[180]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-03-25T00:00:00"}
