{"id":"https://openalex.org/W7135195728","doi":"https://doi.org/10.48550/arxiv.2603.11653","title":"Simple Recipe Works: Vision-Language-Action Models are Natural Continual Learners with Reinforcement Learning","display_name":"Simple Recipe Works: Vision-Language-Action Models are Natural Continual Learners with Reinforcement Learning","publication_year":2026,"publication_date":"2026-03-12","ids":{"openalex":"https://openalex.org/W7135195728","doi":"https://doi.org/10.48550/arxiv.2603.11653"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.11653","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.11653","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.2603.11653","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5128926509","display_name":"Jiaheng Hu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hu, Jiaheng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128937145","display_name":"Jay Shim","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shim, Jay","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128957917","display_name":"Chen Tang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tang, Chen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021197317","display_name":"Yoonchang Sung","orcid":"https://orcid.org/0000-0002-6811-1490"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sung, Yoonchang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129071657","display_name":"Bo Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Bo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128980442","display_name":"Peter Stone","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Stone, Peter","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5129078007","display_name":"Roberto Martin-Martin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Martin-Martin, Roberto","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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.40779998898506165,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.40779998898506165,"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":0.367900013923645,"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/T10462","display_name":"Reinforcement Learning in Robotics","score":0.08950000256299973,"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":0.7613999843597412},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6292999982833862},{"id":"https://openalex.org/keywords/adaptation","display_name":"Adaptation (eye)","score":0.5127999782562256},{"id":"https://openalex.org/keywords/simple","display_name":"Simple (philosophy)","score":0.43470001220703125},{"id":"https://openalex.org/keywords/recipe","display_name":"Recipe","score":0.42250001430511475},{"id":"https://openalex.org/keywords/reinforcement","display_name":"Reinforcement","score":0.37130001187324524},{"id":"https://openalex.org/keywords/lifelong-learning","display_name":"Lifelong learning","score":0.36970001459121704}],"concepts":[{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.7613999843597412},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6764000058174133},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6292999982833862},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6191999912261963},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.5127999782562256},{"id":"https://openalex.org/C2780586882","wikidata":"https://www.wikidata.org/wiki/Q7520643","display_name":"Simple (philosophy)","level":2,"score":0.43470001220703125},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4244999885559082},{"id":"https://openalex.org/C2778671685","wikidata":"https://www.wikidata.org/wiki/Q219239","display_name":"Recipe","level":2,"score":0.42250001430511475},{"id":"https://openalex.org/C67203356","wikidata":"https://www.wikidata.org/wiki/Q1321905","display_name":"Reinforcement","level":2,"score":0.37130001187324524},{"id":"https://openalex.org/C108771440","wikidata":"https://www.wikidata.org/wiki/Q368475","display_name":"Lifelong learning","level":2,"score":0.36970001459121704},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.34689998626708984},{"id":"https://openalex.org/C100609095","wikidata":"https://www.wikidata.org/wiki/Q1335050","display_name":"Embodied cognition","level":2,"score":0.30399999022483826},{"id":"https://openalex.org/C198082294","wikidata":"https://www.wikidata.org/wiki/Q3399648","display_name":"Position (finance)","level":2,"score":0.29330000281333923},{"id":"https://openalex.org/C2776608160","wikidata":"https://www.wikidata.org/wiki/Q4785462","display_name":"Natural (archaeology)","level":2,"score":0.28360000252723694},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.27869999408721924},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.2587999999523163}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.11653","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.11653","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.2603.11653","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.11653","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":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Continual":[0],"Reinforcement":[1],"Learning":[2],"(CRL)":[3],"for":[4,59,150],"Vision-Language-Action":[5],"(VLA)":[6],"models":[7],"is":[8,82,168],"a":[9,49,54,115,147],"promising":[10],"direction":[11],"toward":[12],"self-improving":[13],"embodied":[14],"agents":[15],"that":[16,30,110],"can":[17],"adapt":[18],"in":[19,162],"openended,":[20],"evolving":[21],"environments.":[22],"However,":[23],"conventional":[24],"wisdom":[25],"from":[26,114],"continual":[27,135,151],"learning":[28,161],"suggests":[29],"naive":[31],"Sequential":[32,144],"Fine-Tuning":[33,145],"(Seq.":[34],"FT)":[35],"leads":[36],"to":[37,72,91],"catastrophic":[38],"forgetting,":[39,93],"necessitating":[40],"complex":[41],"CRL":[42,58,103],"strategies.":[43],"In":[44],"this":[45,111],"work,":[46],"we":[47,108],"take":[48],"step":[50],"back":[51],"and":[52,94,124,139,155],"conduct":[53],"systematic":[55],"study":[56],"of":[57],"large":[60,119,164],"pretrained":[61,120],"VLAs":[62,154],"across":[63],"diverse":[64],"lifelong":[65,160],"RL":[66,152],"benchmarks.":[67],"We":[68],"find":[69],"that,":[70],"contrary":[71],"established":[73],"belief,":[74],"simple":[75],"Seq.":[76],"FT":[77],"with":[78,153],"low-rank":[79],"adaptation":[80,136],"(LoRA)":[81],"remarkably":[83],"strong:":[84],"it":[85],"achieves":[86],"high":[87],"plasticity,":[88],"exhibits":[89],"little":[90],"no":[92],"retains":[95],"strong":[96],"zero-shot":[97],"generalization,":[98],"frequently":[99],"outperforming":[100],"more":[101],"sophisticated":[102],"methods.":[104],"Through":[105],"detailed":[106],"analysis,":[107],"show":[109],"robustness":[112],"arises":[113],"synergy":[116],"between":[117],"the":[118,131,163],"model,":[121],"parameter-efficient":[122],"adaptation,":[123],"on-policy":[125],"RL.":[126],"Together,":[127],"these":[128],"components":[129],"reshape":[130],"stability-plasticity":[132],"trade-off,":[133],"making":[134],"both":[137],"stable":[138],"scalable.":[140],"Our":[141],"results":[142],"position":[143],"as":[146],"powerful":[148],"method":[149],"provide":[156],"new":[157],"insights":[158],"into":[159],"model":[165],"era.":[166],"Code":[167],"available":[169],"at":[170],"https://github.com/UT-Austin-RobIn/continual-vla-rl":[171]},"counts_by_year":[],"updated_date":"2026-07-15T05:50:42.429089","created_date":"2026-03-14T00:00:00"}
