{"id":"https://openalex.org/W7160846468","doi":"https://doi.org/10.48550/arxiv.2605.07123","title":"Convergence and Emergence of In-Context Reinforcement Learning with Chain of Thought","display_name":"Convergence and Emergence of In-Context Reinforcement Learning with Chain of Thought","publication_year":2026,"publication_date":"2026-05-08","ids":{"openalex":"https://openalex.org/W7160846468","doi":"https://doi.org/10.48550/arxiv.2605.07123"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.07123","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.07123","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.2605.07123","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5135857734","display_name":"Zixuan Xie","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xie, Zixuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135848873","display_name":"Xinyu Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Xinyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016704715","display_name":"Rohan Chandra","orcid":"https://orcid.org/0000-0003-4843-6375"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chandra, Rohan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5135832492","display_name":"Shangtong Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Shangtong","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":0.7718999981880188,"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":0.7718999981880188,"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/T11883","display_name":"Embodied and Extended Cognition","score":0.03269999846816063,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10581","display_name":"Neural dynamics and brain function","score":0.013100000098347664,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.8172000050544739},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5307999849319458},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.5303999781608582},{"id":"https://openalex.org/keywords/convergence","display_name":"Convergence (economics)","score":0.5139999985694885},{"id":"https://openalex.org/keywords/statistical-inference","display_name":"Statistical inference","score":0.4848000109195709},{"id":"https://openalex.org/keywords/temporal-difference-learning","display_name":"Temporal difference learning","score":0.4763000011444092}],"concepts":[{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.8172000050544739},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5824000239372253},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5307999849319458},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.5303999781608582},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.5139999985694885},{"id":"https://openalex.org/C134261354","wikidata":"https://www.wikidata.org/wiki/Q938438","display_name":"Statistical inference","level":2,"score":0.4848000109195709},{"id":"https://openalex.org/C196340769","wikidata":"https://www.wikidata.org/wiki/Q7698910","display_name":"Temporal difference learning","level":3,"score":0.4763000011444092},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.43790000677108765},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.40849998593330383},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.3878999948501587},{"id":"https://openalex.org/C188116033","wikidata":"https://www.wikidata.org/wiki/Q2664563","display_name":"Q-learning","level":3,"score":0.3693000078201294},{"id":"https://openalex.org/C120936955","wikidata":"https://www.wikidata.org/wiki/Q2155640","display_name":"Empirical research","level":2,"score":0.35929998755455017},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.31769999861717224},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.30570000410079956},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.29010000824928284},{"id":"https://openalex.org/C67203356","wikidata":"https://www.wikidata.org/wiki/Q1321905","display_name":"Reinforcement","level":2,"score":0.27970001101493835},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.26589998602867126}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.07123","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.07123","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.2605.07123","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.07123","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":0.46189355850219727}],"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":[2,87],"(ICRL)":[3],"refers":[4],"to":[5,11,13,46,82],"the":[6,44,76,98,116,123,132,140],"ability":[7],"of":[8,131,143],"RL":[9],"agents":[10],"adapt":[12],"new":[14],"tasks":[15],"at":[16,110],"inference":[17],"time":[18],"without":[19],"parameter":[20],"updates":[21],"by":[22,115],"conditioning":[23],"on":[24,51,139],"additional":[25],"context.":[26],"Recent":[27],"empirical":[28,141],"studies":[29],"further":[30],"demonstrate":[31],"that":[32,71,97,122],"Chain-of-Thought":[33],"(CoT)":[34],"generation":[35,78],"can":[36],"amplify":[37],"this":[38],"ICRL":[39],"capability.":[40],"This":[41],"paper":[42],"is":[43,80],"first":[45],"provide":[47,91],"a":[48,62,111,128,136],"theoretical":[49,137],"understanding":[50,138],"how":[52],"CoT":[53,77,105],"interacts":[54],"with":[55,66,72,104],"ICRL.":[56],"We":[57,69,119],"conduct":[58],"our":[59],"analysis":[60,95],"in":[61],"policy":[63,99],"evaluation":[64,100],"setup":[65],"linear":[67],"Transformer.":[68],"prove":[70,121],"specific":[73],"Transformer":[74,125],"parameters,":[75],"process":[79],"equivalent":[81],"repeatedly":[83],"executing":[84],"temporal":[85],"difference":[86],"updates.":[88],"Additionally,":[89],"we":[90],"finite":[92],"sample":[93],"convergence":[94],"showing":[96],"error":[101],"decreases":[102],"geometrically":[103],"length":[106],"and":[107],"eventually":[108],"saturates":[109],"statistical":[112],"floor":[113],"determined":[114],"context":[117],"length.":[118],"also":[120],"desired":[124],"parameters":[126],"are":[127],"global":[129],"minimizer":[130],"pretraining":[133],"loss,":[134],"providing":[135],"emergence":[142],"those":[144],"parameters.":[145]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-12T00:00:00"}
