{"id":"https://openalex.org/W7162393960","doi":"https://doi.org/10.48550/arxiv.2605.24423","title":"Benchmarking the Limits of In-Context Reinforcement Learning for Ad-Hoc Teamwork","display_name":"Benchmarking the Limits of In-Context Reinforcement Learning for Ad-Hoc Teamwork","publication_year":2026,"publication_date":"2026-05-23","ids":{"openalex":"https://openalex.org/W7162393960","doi":"https://doi.org/10.48550/arxiv.2605.24423"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.24423","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.24423","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.2605.24423","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5019248519","display_name":"Yuheng Jing","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jing, Yuheng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136997325","display_name":"Kai Li","orcid":"https://orcid.org/0009-0009-5885-3114"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Kai","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137028390","display_name":"Ziwen Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Ziwen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137009061","display_name":"Jiajun Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Jiajun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5104334534","display_name":"Zeyao Ma","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ma, Zeyao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137009353","display_name":"Jiaxi Yang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Jiaxi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137063546","display_name":"Lei Zhang","orcid":"https://orcid.org/0009-0004-1681-1956"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Lei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137057112","display_name":"Zhe Wu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wu, Zhe","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5104219889","display_name":"Jinmin He","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"He, Jinmin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137078336","display_name":"Junliang Xing","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xing, Junliang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5137084479","display_name":"Jian Cheng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cheng, Jian","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":0.6966999769210815,"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":0.6966999769210815,"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":0.05209999904036522,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.0502999983727932,"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/benchmarking","display_name":"Benchmarking","score":0.8238000273704529},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.7179999947547913},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.7116000056266785},{"id":"https://openalex.org/keywords/suite","display_name":"Suite","score":0.6327999830245972},{"id":"https://openalex.org/keywords/testbed","display_name":"Testbed","score":0.5457000136375427},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5253000259399414},{"id":"https://openalex.org/keywords/toolbox","display_name":"Toolbox","score":0.492900013923645},{"id":"https://openalex.org/keywords/teamwork","display_name":"Teamwork","score":0.4837999939918518}],"concepts":[{"id":"https://openalex.org/C86251818","wikidata":"https://www.wikidata.org/wiki/Q816754","display_name":"Benchmarking","level":2,"score":0.8238000273704529},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7620000243186951},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.7179999947547913},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.7116000056266785},{"id":"https://openalex.org/C79581498","wikidata":"https://www.wikidata.org/wiki/Q1367530","display_name":"Suite","level":2,"score":0.6327999830245972},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5546000003814697},{"id":"https://openalex.org/C31395832","wikidata":"https://www.wikidata.org/wiki/Q1318674","display_name":"Testbed","level":2,"score":0.5457000136375427},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5410000085830688},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5253000259399414},{"id":"https://openalex.org/C2777655017","wikidata":"https://www.wikidata.org/wiki/Q1501161","display_name":"Toolbox","level":2,"score":0.492900013923645},{"id":"https://openalex.org/C111226992","wikidata":"https://www.wikidata.org/wiki/Q626225","display_name":"Teamwork","level":2,"score":0.4837999939918518},{"id":"https://openalex.org/C173801870","wikidata":"https://www.wikidata.org/wiki/Q201413","display_name":"Heuristic","level":2,"score":0.46059998869895935},{"id":"https://openalex.org/C36299963","wikidata":"https://www.wikidata.org/wiki/Q1369844","display_name":"Observability","level":2,"score":0.38690000772476196},{"id":"https://openalex.org/C177212765","wikidata":"https://www.wikidata.org/wiki/Q627335","display_name":"Workflow","level":2,"score":0.3580000102519989},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.3125999867916107},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.29840001463890076},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2646999955177307},{"id":"https://openalex.org/C182620335","wikidata":"https://www.wikidata.org/wiki/Q2852531","display_name":"Answer set programming","level":3,"score":0.25380000472068787},{"id":"https://openalex.org/C115901376","wikidata":"https://www.wikidata.org/wiki/Q184199","display_name":"Automation","level":2,"score":0.25270000100135803},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.2502000033855438},{"id":"https://openalex.org/C42475967","wikidata":"https://www.wikidata.org/wiki/Q194292","display_name":"Operations research","level":1,"score":0.2500999867916107}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.24423","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.24423","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.2605.24423","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.24423","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/16","display_name":"Peace, Justice and strong institutions","score":0.6628904342651367}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"In-Context":[0],"Reinforcement":[1],"Learning":[2],"(ICRL)":[3],"has":[4],"enabled":[5],"foundation":[6],"agents":[7],"to":[8,11,104,113],"adapt":[9],"instantaneously":[10],"novel":[12],"tasks,":[13],"yet":[14],"its":[15],"efficacy":[16],"in":[17,107,118],"Ad-Hoc":[18],"Teamwork":[19],"(AHT)-where":[20],"coordination":[21,169],"with":[22,136],"unknown":[23],"partners":[24],"is":[25],"required-remains":[26],"unexplored.":[27],"To":[28],"rigorously":[29],"evaluate":[30,82],"this,":[31],"we":[32],"introduce":[33],"a":[34,40,49,66,164],"large-scale":[35],"benchmark":[36,47,162],"ICRL4AHT,":[37],"built":[38],"upon":[39],"high-throughput":[41],"JAX":[42],"implementation":[43],"of":[44,97,149],"Overcooked-V2.":[45],"Our":[46],"includes":[48],"large,":[50],"diverse":[51],"teammate":[52,71,131],"suite":[53],"spanning":[54],"both":[55,129],"RL":[56],"and":[57,64,77,91,132],"heuristic":[58],"policies,":[59],"enabling":[60],"controlled":[61],"train-test":[62],"shifts,":[63],"provides":[65],"reproducible":[67],"end-to-end":[68],"pipeline":[69],"for":[70,167],"generation,":[72],"learning-history":[73],"collection,":[74],"dataset":[75],"construction,":[76],"online":[78],"multi-episode":[79],"evaluation.":[80],"We":[81],"representative":[83],"history-conditioned":[84],"ICRL":[85],"algorithms,":[86],"including":[87],"Algorithm":[88],"Distillation":[89],"(AD)":[90],"Decision-Pretrained":[92],"Transformer":[93],"(DPT),":[94],"across":[95,128],"millions":[96],"transitions.":[98],"Results":[99],"reveal":[100],"notable":[101],"limitations:":[102],"contrary":[103],"their":[105],"success":[106],"single-agent":[108],"domains,":[109],"these":[110,122],"baselines":[111,127],"fail":[112],"exhibit":[114],"robust":[115],"test-time":[116],"adaptation":[117],"multi-agent":[119],"settings.":[120],"Specifically,":[121],"methods":[123],"frequently":[124],"underperform":[125],"random":[126],"unseen":[130,133],"layout":[134],"tracks,":[135],"no":[137],"clear":[138],"in-context":[139],"improvement":[140],"over":[141],"long":[142],"horizons.":[143],"These":[144],"findings":[145],"highlight":[146],"the":[147,156],"challenges":[148],"strategic":[150],"inference":[151],"under":[152],"partial":[153],"observability":[154],"within":[155],"OvercookedV2":[157],"AHT":[158],"protocol,":[159],"establishing":[160],"our":[161],"as":[163],"critical":[165],"testbed":[166],"next-generation":[168],"algorithms.":[170]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-27T00:00:00"}
