{"id":"https://openalex.org/W7160616345","doi":"https://doi.org/10.48550/arxiv.2605.05544","title":"Adaptive Q-Chunking for Offline-to-Online Reinforcement Learning","display_name":"Adaptive Q-Chunking for Offline-to-Online Reinforcement Learning","publication_year":2026,"publication_date":"2026-05-07","ids":{"openalex":"https://openalex.org/W7160616345","doi":"https://doi.org/10.48550/arxiv.2605.05544"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.05544","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.05544","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.2605.05544","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5051376251","display_name":"Nandiraju Gireesh","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gireesh, Nandiraju","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023580129","display_name":"Yunpeng Ju","orcid":"https://orcid.org/0009-0001-1706-0144"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ju, Yuanliang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5135646076","display_name":"He Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, He","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":0.9366999864578247,"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":0.9366999864578247,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.012600000016391277,"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/T12794","display_name":"Adaptive Dynamic Programming Control","score":0.006800000090152025,"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/boosting","display_name":"Boosting (machine learning)","score":0.7300999760627747},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.7235000133514404},{"id":"https://openalex.org/keywords/chunking","display_name":"Chunking (psychology)","score":0.6172999739646912},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.5903000235557556},{"id":"https://openalex.org/keywords/normalization","display_name":"Normalization (sociology)","score":0.365200012922287},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.353300005197525}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7505000233650208},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.7300999760627747},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.7235000133514404},{"id":"https://openalex.org/C203357204","wikidata":"https://www.wikidata.org/wiki/Q1089605","display_name":"Chunking (psychology)","level":2,"score":0.6172999739646912},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6100000143051147},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.5903000235557556},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.512499988079071},{"id":"https://openalex.org/C136886441","wikidata":"https://www.wikidata.org/wiki/Q926129","display_name":"Normalization (sociology)","level":2,"score":0.365200012922287},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.353300005197525},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.33469998836517334},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.2948000133037567},{"id":"https://openalex.org/C2780791683","wikidata":"https://www.wikidata.org/wiki/Q846785","display_name":"Action (physics)","level":2,"score":0.27880001068115234},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.26429998874664307},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.25940001010894775},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.25270000100135803}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.05544","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.05544","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.2605.05544","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.05544","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":"Reduced inequalities","score":0.7435728907585144,"id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Offline-to-online":[0],"reinforcement":[1],"learning":[2],"with":[3],"action":[4,198],"chunking":[5,163],"eliminates":[6],"multi-step":[7],"off-policy":[8],"bias":[9],"and":[10,62,87,136,156,176,182,184],"enables":[11,143],"temporally":[12],"coherent":[13],"exploration,":[14],"but":[15,70],"all":[16],"existing":[17],"methods":[18],"use":[19],"a":[20,113,140],"fixed":[21,166],"chunk":[22,60,82,109,167],"size":[23,110],"across":[24],"every":[25],"state.":[26],"This":[27,121],"is":[28,54],"suboptimal:":[29],"near":[30],"contact":[31],"events":[32],"the":[33,64,80,105,118,151,158,190],"agent":[34],"needs":[35],"short":[36],"chunks":[37,46],"for":[38,58],"reactive":[39],"control,":[40],"while":[41],"during":[42],"free-space":[43],"motion":[44],"long":[45],"provide":[47],"better":[48,144],"credit":[49],"assignment.":[50],"The":[51],"natural":[52],"solution":[53],"to":[55,79,84,89,112,188],"train":[56],"critics":[57],"several":[59],"sizes":[61],"select":[63],"best":[65],"one":[66],"at":[67],"each":[68,108],"state,":[69],"naive":[71],"comparison":[72],"of":[73,107,161,192],"learned":[74],"critic":[75],"values":[76],"systematically":[77],"collapses":[78],"shortest":[81],"due":[83],"discount-scale":[85],"mismatch,":[86],"degrades":[88],"noise":[90,154],"in":[91],"low-value":[92],"states.":[93],"We":[94,146,169],"propose":[95],"Adaptive":[96],"Q-Chunking":[97],"(AQC),":[98],"which":[99],"resolves":[100],"both":[101],"failures":[102],"by":[103,117],"comparing":[104],"advantage":[106,152],"relative":[111],"per-horizon":[114],"baseline,":[115],"normalized":[116],"discount":[119],"factor.":[120],"criterion":[122],"converts":[123],"biased":[124],"wrong":[125],"answers":[126],"into":[127],"unbiased":[128],"near-random":[129],"choices":[130],"when":[131,139],"no":[132],"genuine":[133],"signal":[134],"exists,":[135],"becomes":[137],"discriminative":[138],"particular":[141],"scale":[142],"planning.":[145],"prove":[147],"theoretical":[148],"bounds":[149],"on":[150,157,180,203],"selector's":[153],"immunity":[155],"value":[159],"dominance":[160],"adaptive":[162],"over":[164],"any":[165],"size.":[168],"demonstrate":[170],"that":[171,196],"AQC":[172],"achieves":[173],"state-of-the-art":[174],"offline":[175],"online":[177],"success":[178],"rates":[179],"OGBench":[181],"Robomimic,":[183],"can":[185],"be":[186],"applied":[187],"enhance":[189],"performance":[191,202],"large-scale":[193],"VLA":[194],"models":[195],"predict":[197],"sequences,":[199],"significantly":[200],"boosting":[201],"RoboCasa-GR1":[204],"tasks.":[205]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-09T00:00:00"}