{"id":"https://openalex.org/W7161752353","doi":"https://doi.org/10.48550/arxiv.2605.18803","title":"PROWL: Prioritized Regret-Driven Optimization for World Model Learning","display_name":"PROWL: Prioritized Regret-Driven Optimization for World Model Learning","publication_year":2026,"publication_date":"2026-05-11","ids":{"openalex":"https://openalex.org/W7161752353","doi":"https://doi.org/10.48550/arxiv.2605.18803"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.18803","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.18803","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.2605.18803","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101184429","display_name":"Ahmet H. G\u00fczel","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"G\u00fczel, Ahmet H.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5080892832","display_name":"Jenny Seidenschwarz","orcid":"https://orcid.org/0000-0002-8955-0767"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Seidenschwarz, Jenny","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136602819","display_name":"Benjamin Graham","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Graham, Benjamin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5008731970","display_name":"Jonathan Sadeghi","orcid":"https://orcid.org/0000-0003-4106-2374"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sadeghi, Jonathan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5003749280","display_name":"Jeffrey Hawke","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hawke, Jeffrey","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5136506804","display_name":"Jack Parker-Holder","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bogunovic, Ilija","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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.2736999988555908,"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"}},"topics":[{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.2736999988555908,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.26930001378059387,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.1005999967455864,"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/adversarial-system","display_name":"Adversarial system","score":0.8783000111579895},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6836000084877014},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.510200023651123},{"id":"https://openalex.org/keywords/adversary","display_name":"Adversary","score":0.4041000008583069},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.38440001010894775},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.3693000078201294}],"concepts":[{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.8783000111579895},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7006999850273132},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6836000084877014},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6137999892234802},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.510200023651123},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5005000233650208},{"id":"https://openalex.org/C41065033","wikidata":"https://www.wikidata.org/wiki/Q2825412","display_name":"Adversary","level":2,"score":0.4041000008583069},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.38440001010894775},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.3693000078201294},{"id":"https://openalex.org/C2780791683","wikidata":"https://www.wikidata.org/wiki/Q846785","display_name":"Action (physics)","level":2,"score":0.3422999978065491},{"id":"https://openalex.org/C2776544517","wikidata":"https://www.wikidata.org/wiki/Q189447","display_name":"Unexpected events","level":2,"score":0.3330000042915344},{"id":"https://openalex.org/C2776207758","wikidata":"https://www.wikidata.org/wiki/Q5303302","display_name":"Downstream (manufacturing)","level":2,"score":0.2777999937534332},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.26089999079704285}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.18803","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.18803","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.2605.18803","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.18803","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","id":"https://metadata.un.org/sdg/16","score":0.4733116328716278}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Modern":[0],"action-conditioned":[1],"video":[2],"world":[3,66,76,203],"models":[4,167,204],"achieve":[5],"strong":[6],"short-horizon":[7],"visual":[8],"realism,":[9],"yet":[10],"remain":[11],"unreliable":[12],"on":[13,43,81,109,128,138,159,169,189],"rare,":[14],"interaction-critical":[15],"transitions":[16],"that":[17,91,124,182,201],"dominate":[18],"downstream":[19],"planning":[20],"and":[21,133,156,180],"policy":[22,56],"performance.":[23],"Because":[24],"passive":[25,170],"demonstration":[26],"data":[27,171],"systematically":[28],"under-samples":[29],"these":[30,82],"high-impact":[31],"regimes,":[32],"improving":[33],"robustness":[34,165],"requires":[35],"actively":[36],"eliciting":[37],"model":[38,67,77,114],"failures":[39,94],"rather":[40,142],"than":[41,143],"relying":[42],"their":[44],"natural":[45],"occurrence.":[46],"We":[47,148],"introduce":[48],"a":[49,55,64,96,118],"KL-constrained":[50],"adversarial":[51,88,184],"curriculum":[52],"in":[53,152],"which":[54],"is":[57,78],"trained":[58,168],"to":[59,71],"expose":[60],"high-error":[61],"trajectories":[62,126],"of":[63],"diffusion-based":[65],"while":[68],"remaining":[69],"close":[70],"the":[72,113,153],"behavior":[73],"distribution.":[74],"The":[75],"continuously":[79],"fine-tuned":[80],"adversarially":[83],"discovered":[84],"trajectories,":[85],"yielding":[86],"an":[87],"training":[89,99,137,186,217],"loop":[90],"converts":[92],"rare":[93],"into":[95,103],"stable,":[97],"near-distribution":[98],"signal":[100],"without":[101],"drifting":[102],"out-of-distribution":[104,161],"exploitation.":[105],"To":[106],"maintain":[107],"pressure":[108],"unresolved":[110,139],"weaknesses":[111],"as":[112],"improves,":[115],"we":[116],"propose":[117],"Prioritized":[119],"Adversarial":[120],"Trajectory":[121],"(PAT)":[122],"buffer":[123],"re-ranks":[125],"based":[127],"prediction":[129],"error,":[130],"action":[131],"fidelity,":[132],"learning":[134],"progress,":[135],"focusing":[136],"failure":[140,192],"modes":[141],"repeatedly":[144],"revisiting":[145],"solved":[146],"cases.":[147],"implement":[149],"our":[150],"approach":[151],"MineRL":[154],"framework":[155],"evaluate":[157],"it":[158],"held-out":[160],"trajectories;":[162],"PROWL":[163],"improves":[164],"over":[166],"alone,":[172],"reveals":[173],"reward-hacking":[174],"behaviors":[175],"under":[176],"weak":[177],"behavioral":[178,196],"constraints,":[179],"demonstrates":[181],"effective":[183],"world-model":[185],"critically":[187],"depends":[188],"balancing":[190],"exploratory":[191],"discovery":[193],"with":[194],"explicit":[195],"regularization.":[197],"Our":[198],"results":[199],"suggest":[200],"scalable":[202],"benefit":[205],"not":[206],"only":[207],"from":[208,213],"larger":[209],"datasets,":[210],"but":[211],"also":[212],"selectively":[214],"generating":[215],"informative":[216],"data.":[218]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-21T00:00:00"}