{"id":"https://openalex.org/W7161121331","doi":"https://doi.org/10.48550/arxiv.2605.13013","title":"JEDI: Joint Embedding Diffusion World Model for Online Model-Based Reinforcement Learning","display_name":"JEDI: Joint Embedding Diffusion World Model for Online Model-Based Reinforcement Learning","publication_year":2026,"publication_date":"2026-05-13","ids":{"openalex":"https://openalex.org/W7161121331","doi":"https://doi.org/10.48550/arxiv.2605.13013"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.13013","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.13013","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.2605.13013","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5102545455","display_name":"Jing Yu Lim","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lim, Jing Yu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136144825","display_name":"Rushi Shah","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shah, Rushi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136146632","display_name":"Zarif Ikram","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ikram, Zarif","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064211749","display_name":"Samson Yu","orcid":"https://orcid.org/0000-0003-4647-8539"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yu, Samson","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101323765","display_name":"Haozhe Ma","orcid":"https://orcid.org/0009-0002-7645-0115"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ma, Haozhe","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136125571","display_name":"Tze-Yun Leong","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Leong, Tze-Yun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5136129318","display_name":"Dianbo Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Dianbo","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":0.6996999979019165,"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":0.6996999979019165,"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":0.0632999986410141,"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/T11206","display_name":"Model Reduction and Neural Networks","score":0.02539999969303608,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.6128000020980835},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.5612000226974487},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.5267000198364258},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.4880000054836273},{"id":"https://openalex.org/keywords/diffusion","display_name":"Diffusion","score":0.4415000081062317},{"id":"https://openalex.org/keywords/diffusion-map","display_name":"Diffusion map","score":0.27070000767707825}],"concepts":[{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.6128000020980835},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5773000121116638},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.5612000226974487},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.5267000198364258},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5228999853134155},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.4880000054836273},{"id":"https://openalex.org/C69357855","wikidata":"https://www.wikidata.org/wiki/Q163214","display_name":"Diffusion","level":2,"score":0.4415000081062317},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4146000146865845},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3093999922275543},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2994000017642975},{"id":"https://openalex.org/C55128770","wikidata":"https://www.wikidata.org/wiki/Q5275440","display_name":"Diffusion map","level":4,"score":0.27070000767707825},{"id":"https://openalex.org/C3019722297","wikidata":"https://www.wikidata.org/wiki/Q4440864","display_name":"High dimensional","level":2,"score":0.26820001006126404},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.25519999861717224},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.25360000133514404}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.13013","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.13013","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.2605.13013","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.13013","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],"world":[1,72,112],"models":[2],"have":[3,51,79],"recently":[4],"become":[5],"competitive":[6,174],"for":[7,71,92],"online":[8,108],"model-based":[9],"reinforcement":[10],"learning,":[11],"but":[12,22],"current":[13],"approaches":[14],"expose":[15],"a":[16,89,126,147,156,166,211],"tension:":[17],"pixel":[18,191,219],"diffusion":[19,29,111,122,163,192],"is":[20,173],"effective":[21],"computationally":[23],"expensive":[24],"while":[25],"the":[26,46,106,121,179,190,218],"latest":[27],"latent":[28,110,117],"approach":[30,91],"improves":[31],"efficiency":[32],"yet":[33],"performs":[34],"subpar.":[35],"The":[36],"latter":[37],"also":[38,209],"relies":[39],"on":[40,140,175],"separately":[41],"trained":[42,183],"latents":[43,136,184,225],"rather":[44,137],"than":[45,138,228],"end-to-end":[47,109,223],"world-model":[48,202],"objectives":[49,78,154],"that":[50,151,161,222],"driven":[52],"much":[53],"of":[54],"modern":[55],"MBRL":[56],"progress.":[57],"In":[58],"particular,":[59],"JEPA-style":[60],"predictive":[61,157,224],"representation":[62],"learning":[63],"has":[64],"emerged":[65],"as":[66,88],"an":[67],"especially":[68],"promising":[69,90],"direction":[70],"modeling":[73],"and":[74,94,133,142,160,177,204],"MBRL.":[75],"Concurrently,":[76],"diffusion-style":[77],"gained":[80],"traction":[81],"across":[82],"multiple":[83],"domains,":[84],"with":[85,125,181],"iterative":[86],"refinement":[87],"multimodal":[93],"stochastic":[95],"targets.":[96],"Taken":[97],"together,":[98],"these":[99],"trends":[100],"motivate":[101],"Joint":[102],"Embedding":[103],"DIffusion":[104],"(JEDI),":[105],"first":[107],"model.":[113],"JEDI":[114,172,194,208],"learns":[115],"its":[116],"space":[118],"directly":[119,186],"from":[120,217],"denoising":[123,130,164],"loss":[124],"JEPA":[127,153],"framework,":[128],"using":[129],"to":[131,189],"learn":[132],"predict":[134],"future":[135],"relying":[139],"reconstruction":[141],"pretrained":[143],"models.":[144],"We":[145],"provide":[146],"theoretical":[148],"motivation":[149],"showing":[150],"conventional":[152],"induce":[155],"information":[158],"bottleneck,":[159],"conditional":[162],"admits":[165],"closely":[167],"related":[168],"predictive-compression":[169],"decomposition.":[170],"Empirically,":[171],"Atari100k":[176],"outperforms":[178],"baseline":[180],"seperately":[182],"where":[185],"comparable.":[187],"Relative":[188],"baseline,":[193,220],"uses":[195],"43%":[196],"less":[197],"VRAM,":[198],"over":[199],"3$\\times$":[200],"faster":[201,206],"sampling,":[203],"2.5$\\times$":[205],"training.":[207],"exhibits":[210],"markedly":[212],"different":[213],"task-level":[214],"performance":[215],"profile":[216],"suggesting":[221],"change":[226],"more":[227],"compute":[229],"alone.":[230]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-15T00:00:00"}