{"id":"https://openalex.org/W7133351785","doi":"https://doi.org/10.48550/arxiv.2603.01063","title":"Unleashing VLA Potentials in Autonomous Driving via Explicit Learning from Failures","display_name":"Unleashing VLA Potentials in Autonomous Driving via Explicit Learning from Failures","publication_year":2026,"publication_date":"2026-03-01","ids":{"openalex":"https://openalex.org/W7133351785","doi":"https://doi.org/10.48550/arxiv.2603.01063"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.01063","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.01063","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":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.01063","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5127912990","display_name":"Yuechen Luo","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Luo, Yuechen","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5114085650","display_name":"Qimao Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Qimao","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5127879707","display_name":"Fang Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Fang","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5127907773","display_name":"Shaoqing Xu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xu, Shaoqing","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5127943561","display_name":"Jaxin Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Jaxin","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5114244653","display_name":"Ziying Song","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Song, Ziying","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103102258","display_name":"Zhixin Yang","orcid":"https://orcid.org/0000-0002-7901-3829"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Zhi-xin","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":null,"display_name":"Wen, Fuxi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wen, Fuxi","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":8,"corresponding_author_ids":["https://openalex.org/A5127912990"],"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/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.32749998569488525,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.32749998569488525,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10462","display_name":"Reinforcement Learning in Robotics","score":0.25780001282691956,"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":0.15389999747276306,"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":0.7243000268936157},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.498199999332428},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.4230000078678131},{"id":"https://openalex.org/keywords/root","display_name":"Root (linguistics)","score":0.3776000142097473},{"id":"https://openalex.org/keywords/root-cause","display_name":"Root cause","score":0.3693000078201294},{"id":"https://openalex.org/keywords/policy-learning","display_name":"Policy learning","score":0.3675000071525574},{"id":"https://openalex.org/keywords/supervised-learning","display_name":"Supervised learning","score":0.3260999917984009}],"concepts":[{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.7243000268936157},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6093000173568726},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5566999912261963},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.498199999332428},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4918000102043152},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.4230000078678131},{"id":"https://openalex.org/C171078966","wikidata":"https://www.wikidata.org/wiki/Q111029","display_name":"Root (linguistics)","level":2,"score":0.3776000142097473},{"id":"https://openalex.org/C84945661","wikidata":"https://www.wikidata.org/wiki/Q7366567","display_name":"Root cause","level":2,"score":0.3693000078201294},{"id":"https://openalex.org/C2779436431","wikidata":"https://www.wikidata.org/wiki/Q30672407","display_name":"Policy learning","level":2,"score":0.3675000071525574},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.3260999917984009},{"id":"https://openalex.org/C47932503","wikidata":"https://www.wikidata.org/wiki/Q5395689","display_name":"Error-driven learning","level":3,"score":0.3230000138282776},{"id":"https://openalex.org/C2776544517","wikidata":"https://www.wikidata.org/wiki/Q189447","display_name":"Unexpected events","level":2,"score":0.30140000581741333},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.3012999892234802},{"id":"https://openalex.org/C2780735816","wikidata":"https://www.wikidata.org/wiki/Q28324931","display_name":"Incremental learning","level":2,"score":0.2906000018119812},{"id":"https://openalex.org/C130963320","wikidata":"https://www.wikidata.org/wiki/Q1401207","display_name":"Root cause analysis","level":2,"score":0.2773999869823456},{"id":"https://openalex.org/C34585555","wikidata":"https://www.wikidata.org/wiki/Q1368723","display_name":"Learning curve","level":2,"score":0.27250000834465027},{"id":"https://openalex.org/C13687954","wikidata":"https://www.wikidata.org/wiki/Q4826847","display_name":"Autonomous agent","level":2,"score":0.26489999890327454}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.01063","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.01063","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":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.01063","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.01063","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":"article"},"sustainable_development_goals":[{"display_name":"No poverty","id":"https://metadata.un.org/sdg/1","score":0.4747362732887268}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Vision-Language-Action":[0],"(VLA)":[1],"models":[2],"for":[3,181],"autonomous":[4],"driving":[5,45],"often":[6],"hit":[7],"a":[8,43,51,87,100,126,144],"performance":[9,175],"plateau":[10],"during":[11],"Reinforcement":[12],"Learning":[13,83],"(RL)":[14],"optimization.":[15],"This":[16,47],"stagnation":[17],"arises":[18],"from":[19,84],"exploration":[20,157],"capabilities":[21,168],"constrained":[22],"by":[23],"previous":[24],"Supervised":[25],"Fine-Tuning":[26],"(SFT),":[27],"leading":[28],"to":[29,55,65,124,151],"persistent":[30],"failures":[31],"in":[32],"long-tail":[33],"scenarios.":[34],"In":[35],"these":[36,131],"critical":[37,153],"situations,":[38],"all":[39],"explored":[40],"actions":[41],"yield":[42],"zero-value":[44],"score.":[46],"information-sparse":[48],"reward":[49],"signals":[50],"failure,":[52],"yet":[53],"fails":[54],"identify":[56,111],"its":[57],"root":[58],"cause":[59],"--":[60],"whether":[61],"it":[62],"is":[63],"due":[64],"incorrect":[66],"planning,":[67],"flawed":[68],"reasoning,":[69],"or":[70],"poor":[71],"trajectory":[72],"execution.":[73],"To":[74],"address":[75],"this":[76,121],"limitation,":[77],"we":[78],"propose":[79],"VLA":[80,117,170],"with":[81,92],"Explicit":[82],"Failures":[85],"(ELF-VLA),":[86],"framework":[88],"that":[89,110,155,162],"augments":[90],"RL":[91,138],"structured":[93],"diagnostic":[94],"feedback.":[95],"Instead":[96],"of":[97,169],"relying":[98],"on":[99,176],"vague":[101],"scalar":[102],"reward,":[103],"our":[104,141,163],"method":[105,164],"produces":[106],"detailed,":[107],"interpretable":[108],"reports":[109],"the":[112,137,149,166,177],"specific":[113],"failure":[114],"mode.":[115],"The":[116],"policy":[118,150],"then":[119],"leverages":[120],"explicit":[122],"feedback":[123],"generate":[125],"Feedback-Guided":[127],"Refinement.":[128],"By":[129],"injecting":[130],"corrected,":[132],"high-reward":[133],"samples":[134],"back":[135],"into":[136],"training":[139],"batch,":[140],"approach":[142],"provides":[143],"targeted":[145],"gradient,":[146],"which":[147],"enables":[148],"solve":[152],"scenarios":[154],"unguided":[156],"cannot.":[158],"Extensive":[159],"experiments":[160],"demonstrate":[161],"unlocks":[165],"latent":[167],"models,":[171],"achieving":[172],"state-of-the-art":[173],"(SOTA)":[174],"public":[178],"NAVSIM":[179],"benchmark":[180],"overall":[182],"PDMS,":[183],"EPDMS":[184],"score":[185],"and":[186],"high-level":[187],"planning":[188],"accuracy.":[189]},"counts_by_year":[],"updated_date":"2026-03-25T23:56:10.502304","created_date":"2026-03-04T00:00:00"}