{"id":"https://openalex.org/W7159644699","doi":"https://doi.org/10.48550/arxiv.2604.27859","title":"Rethinking Agentic Reinforcement Learning In Large Language Models","display_name":"Rethinking Agentic Reinforcement Learning In Large Language Models","publication_year":2026,"publication_date":"2026-04-30","ids":{"openalex":"https://openalex.org/W7159644699","doi":"https://doi.org/10.48550/arxiv.2604.27859"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.27859","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.27859","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.2604.27859","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5134974079","display_name":"Fangming Cui","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cui, Fangming","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134946438","display_name":"Ruixiao Zhu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhu, Ruixiao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100664794","display_name":"Cheng Fang","orcid":"https://orcid.org/0000-0001-7293-9082"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fang, Cheng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134948256","display_name":"Sunan Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Sunan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5134932015","display_name":"Jiahong Li (1608247)","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Jiahong","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":0.12800000607967377,"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":0.12800000607967377,"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":0.1062999963760376,"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":0.07800000160932541,"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/reinforcement-learning","display_name":"Reinforcement learning","score":0.6518999934196472},{"id":"https://openalex.org/keywords/language-understanding","display_name":"Language understanding","score":0.3939000070095062},{"id":"https://openalex.org/keywords/function","display_name":"Function (biology)","score":0.3531000018119812},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.32260000705718994},{"id":"https://openalex.org/keywords/variety","display_name":"Variety (cybernetics)","score":0.2921999990940094},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.2818000018596649},{"id":"https://openalex.org/keywords/language-acquisition","display_name":"Language acquisition","score":0.27880001068115234}],"concepts":[{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.6518999934196472},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5852000117301941},{"id":"https://openalex.org/C188147891","wikidata":"https://www.wikidata.org/wiki/Q147638","display_name":"Cognitive science","level":1,"score":0.4943999946117401},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.42410001158714294},{"id":"https://openalex.org/C2983448237","wikidata":"https://www.wikidata.org/wiki/Q1078276","display_name":"Language understanding","level":2,"score":0.3939000070095062},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.3531000018119812},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.3246999979019165},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.32260000705718994},{"id":"https://openalex.org/C180747234","wikidata":"https://www.wikidata.org/wiki/Q23373","display_name":"Cognitive psychology","level":1,"score":0.3127000033855438},{"id":"https://openalex.org/C136197465","wikidata":"https://www.wikidata.org/wiki/Q1729295","display_name":"Variety (cybernetics)","level":2,"score":0.2921999990940094},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.2818000018596649},{"id":"https://openalex.org/C74672266","wikidata":"https://www.wikidata.org/wiki/Q815859","display_name":"Language acquisition","level":2,"score":0.27880001068115234},{"id":"https://openalex.org/C169900460","wikidata":"https://www.wikidata.org/wiki/Q2200417","display_name":"Cognition","level":2,"score":0.2750999927520752},{"id":"https://openalex.org/C67203356","wikidata":"https://www.wikidata.org/wiki/Q1321905","display_name":"Reinforcement","level":2,"score":0.2741999924182892},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.2728999853134155},{"id":"https://openalex.org/C2780791683","wikidata":"https://www.wikidata.org/wiki/Q846785","display_name":"Action (physics)","level":2,"score":0.26499998569488525},{"id":"https://openalex.org/C37228920","wikidata":"https://www.wikidata.org/wiki/Q1307600","display_name":"Experiential learning","level":2,"score":0.26170000433921814},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.2606000006198883},{"id":"https://openalex.org/C2779439875","wikidata":"https://www.wikidata.org/wiki/Q1078276","display_name":"Natural language understanding","level":3,"score":0.25999999046325684},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.2524999976158142},{"id":"https://openalex.org/C77967617","wikidata":"https://www.wikidata.org/wiki/Q4677561","display_name":"Active learning (machine learning)","level":2,"score":0.250900000333786}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.27859","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.27859","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.2604.27859","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.27859","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":[{"id":"https://metadata.un.org/sdg/16","score":0.666373074054718,"display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Reinforcement":[0],"Learning":[1],"(RL)":[2],"has":[3,33],"traditionally":[4],"focused":[5],"on":[6,78],"training":[7],"specialized":[8],"agents":[9,56],"to":[10],"optimize":[11],"predefined":[12],"reward":[13],"functions":[14],"within":[15,41],"narrowly":[16],"defined":[17],"environments.":[18,71],"However,":[19],"the":[20,52,99,112],"advent":[21],"of":[22,54,58],"powerful":[23],"Large":[24],"Language":[25],"Models":[26],"(LLMs)":[27],"and":[28,65,81,94,117,128],"increasingly":[29],"complex,":[30],"open-ended":[31],"tasks":[32],"catalyzed":[34],"a":[35,107],"paradigm":[36],"shift":[37],"towards":[38],"agentic":[39],"paradigms":[40],"RL.":[42,137],"This":[43],"emerging":[44],"framework":[45],"extends":[46],"beyond":[47],"traditional":[48],"RL":[49,86],"by":[50],"emphasizing":[51],"development":[53],"autonomous":[55],"capable":[57],"goal-setting,":[59],"long-term":[60],"planning,":[61],"dynamic":[62],"strategy":[63],"adaptation,":[64],"interactive":[66],"reasoning":[67],"in":[68],"uncertain,":[69],"real-world":[70],"Unlike":[72],"conventional":[73],"approaches":[74],"that":[75],"rely":[76],"heavily":[77],"static":[79],"objectives":[80],"episodic":[82],"interactions,":[83],"LLM-based":[84,135],"Agentic":[85,136],"incorporates":[87],"cognitive-like":[88],"capabilities":[89],"such":[90],"as":[91],"meta-reasoning,":[92],"self-reflection,":[93],"multi-step":[95],"decision-making":[96],"directly":[97],"into":[98],"learning":[100],"loop.":[101],"In":[102],"this":[103,121],"paper,":[104],"we":[105,124],"provide":[106],"deep":[108],"insight":[109],"for":[110,133],"looking":[111],"conceptual":[113],"foundations,":[114],"methodological":[115],"innovations,":[116],"effective":[118],"designs":[119],"underlying":[120],"trend.":[122],"Furthermore,":[123],"identify":[125],"critical":[126],"challenges":[127],"outline":[129],"promising":[130],"future":[131],"directions":[132],"building":[134]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-02T00:00:00"}
