{"id":"https://openalex.org/W7161289579","doi":"https://doi.org/10.48550/arxiv.2605.14220","title":"Diagnosing Training Inference Mismatch in LLM Reinforcement Learning","display_name":"Diagnosing Training Inference Mismatch in LLM Reinforcement Learning","publication_year":2026,"publication_date":"2026-05-14","ids":{"openalex":"https://openalex.org/W7161289579","doi":"https://doi.org/10.48550/arxiv.2605.14220"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.14220","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.14220","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.2605.14220","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5021923092","display_name":"Tianle Zhong","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhong, Tianle","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049748462","display_name":"Neiwen Ling","orcid":"https://orcid.org/0000-0003-2072-1502"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ling, Neiwen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5055717204","display_name":"Yifan Pi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pi, Yifan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136246244","display_name":"Zijun Wei","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wei, Zijun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136265380","display_name":"Tianshu Yu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yu, Tianshu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136189137","display_name":"Geoffrey Fox","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fox, Geoffrey","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136203021","display_name":"Peng Wu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wu, Peng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5113429820","display_name":"Xiao Yu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yu, Xiao","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":0.7376999855041504,"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":0.7376999855041504,"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/T11975","display_name":"Evolutionary Algorithms and Applications","score":0.08910000324249268,"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/T10142","display_name":"Formal Methods in Verification","score":0.015399999916553497,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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/security-token","display_name":"Security token","score":0.6650000214576721},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.6019999980926514},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5770000219345093},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5479999780654907},{"id":"https://openalex.org/keywords/sequence","display_name":"Sequence (biology)","score":0.4287000000476837},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.41999998688697815}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6847000122070312},{"id":"https://openalex.org/C48145219","wikidata":"https://www.wikidata.org/wiki/Q1335365","display_name":"Security token","level":2,"score":0.6650000214576721},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.6019999980926514},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5770000219345093},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5479999780654907},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5184999704360962},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.4287000000476837},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.41999998688697815},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.3894999921321869},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.384799987077713},{"id":"https://openalex.org/C177918212","wikidata":"https://www.wikidata.org/wiki/Q803623","display_name":"Perturbation (astronomy)","level":2,"score":0.3370000123977661},{"id":"https://openalex.org/C22367795","wikidata":"https://www.wikidata.org/wiki/Q7625208","display_name":"Structured prediction","level":2,"score":0.27090001106262207}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.14220","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.14220","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.2605.14220","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.14220","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","score":0.474618136882782,"id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Modern":[0],"LLM":[1,128],"RL":[2,129],"systems":[3],"separate":[4],"rollout":[5],"generation":[6],"from":[7],"policy":[8],"optimization.":[9],"These":[10],"two":[11],"stages":[12],"are":[13],"expected":[14],"to":[15,31,47],"produce":[16],"token":[17],"probabilities":[18],"that":[19,74,87,100,107,118],"match":[20],"exactly.":[21],"However,":[22],"implementation":[23],"differences":[24],"can":[25,79],"make":[26],"them":[27],"assign":[28],"different":[29],"values":[30],"the":[32,36,90],"same":[33,37],"sequence":[34],"under":[35],"model":[38],"weights,":[39],"inducing":[40],"Training-Inference":[41],"Mismatch":[42],"(TIM).":[43],"TIM":[44,65,88,108],"is":[45,51,109],"difficult":[46],"inspect":[48],"because":[49],"it":[50],"entangled":[52],"with":[53],"off-policy":[54],"drift":[55],"and":[56,72,94],"common":[57],"stabilization":[58],"mechanisms.":[59],"In":[60],"this":[61],"work,":[62],"we":[63],"isolate":[64],"in":[66,126],"a":[67,96,115,123],"zero-mismatch":[68],"diagnostic":[69],"setting":[70],"(VeXact),":[71],"show":[73,86],"small":[75],"token-level":[76],"numerical":[77,112],"disagreements":[78],"independently":[80],"cause":[81],"training":[82],"collapse.":[83],"We":[84],"further":[85],"changes":[89],"effective":[91],"optimization":[92],"problem,":[93],"identify":[95],"set":[97],"of":[98],"remedies":[99],"could":[101],"mitigate":[102],"TIM.":[103],"Our":[104],"results":[105],"suggest":[106],"not":[110],"benign":[111],"noise,":[113],"but":[114],"systems-level":[116],"perturbation":[117],"should":[119],"be":[120],"treated":[121],"as":[122],"first-order":[124],"factor":[125],"analyzing":[127],"stability.":[130]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-16T00:00:00"}
