{"id":"https://openalex.org/W7160015970","doi":"https://doi.org/10.48550/arxiv.2605.00623","title":"Recovering Hidden Reward in Diffusion-Based Policies","display_name":"Recovering Hidden Reward in Diffusion-Based Policies","publication_year":2026,"publication_date":"2026-05-01","ids":{"openalex":"https://openalex.org/W7160015970","doi":"https://doi.org/10.48550/arxiv.2605.00623"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.00623","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.00623","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.2605.00623","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5025961615","display_name":"Yuhua Ji","orcid":"https://orcid.org/0000-0002-6127-7287"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ji, Yanbiao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135234985","display_name":"Qiuchang Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Qiuchang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073854712","display_name":"Yuting Hu","orcid":"https://orcid.org/0000-0002-8153-595X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hu, Yuting","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135192258","display_name":"Shaokai Wu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wu, Shaokai","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5120717866","display_name":"Wenyuan Xie","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xie, Wenyuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135224616","display_name":"Guodong Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Guodong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135156797","display_name":"Qicheng He","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"He, Qicheng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135248646","display_name":"Deyi Ji","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ji, Deyi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135128105","display_name":"Yue Ding","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ding, Yue","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5102899381","display_name":"Hongtao Lu","orcid":"https://orcid.org/0000-0003-2300-3039"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lu, Hongtao","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":0.5702999830245972,"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":0.5702999830245972,"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":0.08250000327825546,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.06790000200271606,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.6096000075340271},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.4927999973297119},{"id":"https://openalex.org/keywords/action","display_name":"Action (physics)","score":0.484499990940094},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.48170000314712524},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.4747999906539917},{"id":"https://openalex.org/keywords/identifiability","display_name":"Identifiability","score":0.461899995803833},{"id":"https://openalex.org/keywords/function","display_name":"Function (biology)","score":0.44020000100135803},{"id":"https://openalex.org/keywords/score","display_name":"Score","score":0.36039999127388},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.34880000352859497}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6717000007629395},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6247000098228455},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.6096000075340271},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5228000283241272},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.4927999973297119},{"id":"https://openalex.org/C2780791683","wikidata":"https://www.wikidata.org/wiki/Q846785","display_name":"Action (physics)","level":2,"score":0.484499990940094},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.48170000314712524},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.4747999906539917},{"id":"https://openalex.org/C122770356","wikidata":"https://www.wikidata.org/wiki/Q1656753","display_name":"Identifiability","level":2,"score":0.461899995803833},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.44020000100135803},{"id":"https://openalex.org/C65660741","wikidata":"https://www.wikidata.org/wiki/Q3952743","display_name":"Score","level":2,"score":0.36039999127388},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.34880000352859497},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.32659998536109924},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.31709998846054077},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.30329999327659607},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.295199990272522},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.2937999963760376},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.27149999141693115},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.2703999876976013},{"id":"https://openalex.org/C22367795","wikidata":"https://www.wikidata.org/wiki/Q7625208","display_name":"Structured prediction","level":2,"score":0.2687000036239624},{"id":"https://openalex.org/C126388530","wikidata":"https://www.wikidata.org/wiki/Q1131737","display_name":"Imitation","level":2,"score":0.2648000121116638},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.2581000030040741},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.2565000057220459},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.2522999942302704}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.00623","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.00623","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.2605.00623","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.00623","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":[{"id":"https://metadata.un.org/sdg/7","score":0.7369174361228943,"display_name":"Affordable and clean energy"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"This":[0],"paper":[1],"introduces":[2],"EnergyFlow,":[3],"a":[4,17],"framework":[5],"that":[6,29,58,112,124],"unifies":[7],"generative":[8],"action":[9,90],"modeling":[10],"with":[11],"inverse":[12],"reinforcement":[13,110],"learning":[14,111],"by":[15],"parameterizing":[16],"scalar":[18],"energy":[19],"function":[20,35],"whose":[21],"gradient":[22,43],"is":[23,144],"the":[24,33,42,45,60,77,125],"denoising":[25,38],"field.":[26],"We":[27,74],"establish":[28],"under":[30],"maximum-entropy":[31],"optimality,":[32],"score":[34,39,85],"learned":[36,61],"via":[37],"matching":[40],"recovers":[41],"of":[44,79],"expert's":[46],"soft":[47],"Q-function,":[48],"enabling":[49],"reward":[50,106,131],"extraction":[51,132],"without":[52],"adversarial":[53,115],"training.":[54],"Formally,":[55],"we":[56],"prove":[57],"constraining":[59],"field":[62],"to":[63,89],"be":[64],"conservative":[65],"reduces":[66],"hypothesis":[67],"complexity":[68],"and":[69,82,118],"tightens":[70],"out-of-distribution":[71],"generalization":[72],"bounds.":[73],"further":[75],"characterize":[76],"identifiability":[78],"recovered":[80],"rewards":[81],"bound":[83],"how":[84],"estimation":[86],"errors":[87],"propagate":[88],"preferences.":[91],"Empirically,":[92],"EnergyFlow":[93],"achieves":[94],"state-of-the-art":[95],"imitation":[96],"performance":[97],"on":[98],"various":[99],"manipulation":[100],"tasks":[101],"while":[102],"providing":[103],"an":[104],"effective":[105],"signal":[107],"for":[108,129,139],"downstream":[109],"outperforms":[113],"both":[114],"IRL":[116],"methods":[117],"likelihood-based":[119],"alternatives.":[120],"These":[121],"results":[122],"show":[123],"structural":[126],"constraints":[127],"required":[128],"valid":[130],"simultaneously":[133],"serve":[134],"as":[135],"beneficial":[136],"inductive":[137],"biases":[138],"policy":[140],"generalization.":[141],"The":[142],"code":[143],"available":[145],"at":[146],"https://github.com/sotaagi/EnergyFlow.":[147]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-05T00:00:00"}