{"id":"https://openalex.org/W7165613746","doi":"https://doi.org/10.48550/arxiv.2606.22056","title":"Provably Efficient Policy-Reward Co-Pretraining for Adversarial Imitation Learning","display_name":"Provably Efficient Policy-Reward Co-Pretraining for Adversarial Imitation Learning","publication_year":2026,"publication_date":"2026-06-20","ids":{"openalex":"https://openalex.org/W7165613746","doi":"https://doi.org/10.48550/arxiv.2606.22056"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.22056","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.22056","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.2606.22056","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5139168526","display_name":"Tian Xu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xu, Tian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124015328","display_name":"Zexuan Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Zexuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139147975","display_name":"Zhilong Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Zhilong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139179745","display_name":"Yi-Chen Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Yi-Chen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139208291","display_name":"Chenyang Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Chenyang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139145751","display_name":"Lei Yuan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yuan, Lei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5139155266","display_name":"Yang Yu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yu, Yang","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":0.5874999761581421,"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":0.5874999761581421,"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":0.09849999845027924,"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"}},{"id":"https://openalex.org/T10709","display_name":"Social Robot Interaction and HRI","score":0.05299999937415123,"subfield":{"id":"https://openalex.org/subfields/3207","display_name":"Social Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.760699987411499},{"id":"https://openalex.org/keywords/imitation","display_name":"Imitation","score":0.616599977016449},{"id":"https://openalex.org/keywords/initialization","display_name":"Initialization","score":0.5375000238418579},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.412200003862381},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.31529998779296875},{"id":"https://openalex.org/keywords/margin","display_name":"Margin (machine learning)","score":0.3021000027656555}],"concepts":[{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.760699987411499},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7027000188827515},{"id":"https://openalex.org/C126388530","wikidata":"https://www.wikidata.org/wiki/Q1131737","display_name":"Imitation","level":2,"score":0.616599977016449},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5726000070571899},{"id":"https://openalex.org/C114466953","wikidata":"https://www.wikidata.org/wiki/Q6034165","display_name":"Initialization","level":2,"score":0.5375000238418579},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.427700012922287},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.412200003862381},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.31529998779296875},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.3021000027656555},{"id":"https://openalex.org/C120936955","wikidata":"https://www.wikidata.org/wiki/Q2155640","display_name":"Empirical research","level":2,"score":0.2867000102996826},{"id":"https://openalex.org/C121050878","wikidata":"https://www.wikidata.org/wiki/Q5135020","display_name":"Cloning (programming)","level":2,"score":0.2833000123500824},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.2802000045776367},{"id":"https://openalex.org/C18762648","wikidata":"https://www.wikidata.org/wiki/Q42213","display_name":"Work (physics)","level":2,"score":0.2711000144481659},{"id":"https://openalex.org/C180747234","wikidata":"https://www.wikidata.org/wiki/Q23373","display_name":"Cognitive psychology","level":1,"score":0.25859999656677246}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.22056","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.22056","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.2606.22056","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.22056","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":{"Adversarial":[0],"imitation":[1,6,148],"learning":[2],"(AIL)":[3],"achieves":[4,145],"high-quality":[5],"compared":[7],"to":[8,30,125],"behavioral":[9],"cloning":[10],"(BC),":[11],"but":[12],"demands":[13],"substantial":[14],"online":[15],"environment":[16],"interaction.":[17],"Recent":[18],"empirical":[19],"work":[20],"has":[21],"explored":[22],"initializing":[23],"AIL":[24,43,65,174],"algorithms":[25,57],"with":[26,66],"BC":[27,139],"pretrained":[28],"policies":[29,117],"address":[31],"this":[32,94],"limitation,":[33],"yet":[34],"a":[35,49,81,98,105,112,137],"rigorous":[36],"theoretical":[37,51,157],"understanding":[38],"of":[39,77,89,162],"pretraining's":[40],"role":[41],"in":[42,104,164],"remains":[44],"elusive.":[45],"This":[46,79],"paper":[47],"provides":[48],"systematic":[50],"analysis":[52,110],"and":[53,83,118,134],"introduces":[54],"principled":[55,99],"pretraining":[56,68,163],"for":[58,159],"accelerating":[59],"AIL.":[60,165],"We":[61,141],"begin":[62],"by":[63,93],"analyzing":[64],"policy":[67,133],"alone,":[69],"identifying":[70],"reward":[71,90,106,135],"error":[72],"as":[73],"the":[74,87,155,160],"dominant":[75],"source":[76],"suboptimality.":[78],"reveals":[80],"critical":[82],"previously":[84],"overlooked":[85],"gap:":[86],"absence":[88],"pretraining.":[91],"Motivated":[92],"finding,":[95],"we":[96],"develop":[97],"policy-reward":[100],"co-pretraining":[101],"approach":[102,128],"grounded":[103],"shaping":[107,119],"analysis.":[108],"Our":[109],"uncovers":[111],"fundamental":[113],"connection":[114],"between":[115],"expert":[116],"rewards,":[120],"which":[121],"naturally":[122],"gives":[123],"rise":[124],"CoPT-AIL,":[126],"an":[127,146],"that":[129,143],"jointly":[130],"pretrains":[131],"both":[132],"through":[136],"single":[138],"procedure.":[140],"prove":[142],"CoPT-AIL":[144],"improved":[147],"gap":[149],"bound":[150],"over":[151,172],"standard":[152],"AIL,":[153],"establishing":[154],"first":[156],"guarantee":[158],"benefits":[161],"Experimental":[166],"results":[167],"confirm":[168],"CoPT-AIL's":[169],"superior":[170],"performance":[171],"existing":[173],"methods.":[175]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-24T00:00:00"}