{"id":"https://openalex.org/W7162448982","doi":"https://doi.org/10.48550/arxiv.2605.25604","title":"DVAO: Dynamic Variance-adaptive Advantage Optimization for Multi-reward Reinforcement Learning","display_name":"DVAO: Dynamic Variance-adaptive Advantage Optimization for Multi-reward Reinforcement Learning","publication_year":2026,"publication_date":"2026-05-25","ids":{"openalex":"https://openalex.org/W7162448982","doi":"https://doi.org/10.48550/arxiv.2605.25604"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.25604","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.25604","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.2605.25604","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5109715043","display_name":"Guochao Jiang","orcid":"https://orcid.org/0009-0002-3415-4473"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jiang, Guochao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137077746","display_name":"Jingyi Song","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Song, Jingyi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137004253","display_name":"Guofeng Quan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Quan, Guofeng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5104152397","display_name":"Chuzhan Hao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hao, Chuzhan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137015717","display_name":"Guohua Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Guohua","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5023037580","display_name":"Yuewei Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Yuewei","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":0.43309998512268066,"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":0.43309998512268066,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.06939999759197235,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.05480000004172325,"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":0.7208999991416931},{"id":"https://openalex.org/keywords/hyperparameter","display_name":"Hyperparameter","score":0.576200008392334},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.4771000146865845},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.4580000042915344},{"id":"https://openalex.org/keywords/pareto-principle","display_name":"Pareto principle","score":0.45399999618530273},{"id":"https://openalex.org/keywords/variance","display_name":"Variance (accounting)","score":0.4115000069141388},{"id":"https://openalex.org/keywords/baseline","display_name":"Baseline (sea)","score":0.390500009059906},{"id":"https://openalex.org/keywords/black-box","display_name":"Black box","score":0.35010001063346863}],"concepts":[{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.7208999991416931},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6897000074386597},{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.576200008392334},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5472999811172485},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5110999941825867},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.4771000146865845},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.4580000042915344},{"id":"https://openalex.org/C137635306","wikidata":"https://www.wikidata.org/wiki/Q182667","display_name":"Pareto principle","level":2,"score":0.45399999618530273},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.4115000069141388},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.390500009059906},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3749000132083893},{"id":"https://openalex.org/C94966114","wikidata":"https://www.wikidata.org/wiki/Q29256","display_name":"Black box","level":2,"score":0.35010001063346863},{"id":"https://openalex.org/C34388435","wikidata":"https://www.wikidata.org/wiki/Q2267362","display_name":"Bounded function","level":2,"score":0.32010000944137573},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.3046000003814697},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.2962999939918518},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.28600001335144043},{"id":"https://openalex.org/C68781425","wikidata":"https://www.wikidata.org/wiki/Q2052203","display_name":"Multi-objective optimization","level":2,"score":0.2847000062465668},{"id":"https://openalex.org/C120936955","wikidata":"https://www.wikidata.org/wiki/Q2155640","display_name":"Empirical research","level":2,"score":0.26260000467300415},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.2542000114917755},{"id":"https://openalex.org/C196340769","wikidata":"https://www.wikidata.org/wiki/Q7698910","display_name":"Temporal difference learning","level":3,"score":0.25220000743865967},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.25119999051094055}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.25604","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.25604","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.2605.25604","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.25604","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":{"Reinforcement":[0],"Learning":[1],"has":[2],"become":[3],"a":[4,106,113,135,161],"standard":[5],"paradigm":[6],"for":[7,130],"aligning":[8],"Large":[9],"Language":[10],"Models":[11],"with":[12,59,112],"human":[13],"intent":[14],"and":[15,47,76,133,145,150,166],"task":[16],"requirements.":[17],"While":[18],"Group":[19],"Relative":[20],"Policy":[21,30],"Optimization":[22,89],"offers":[23],"an":[24],"efficient,":[25],"value-model-free":[26],"alternative":[27],"to":[28,34,66],"Proximal":[29],"Optimization,":[31],"adapting":[32],"it":[33],"real-world":[35],"multi-reward":[36],"settings":[37],"remains":[38],"challenging.":[39],"Standard":[40],"scalarization":[41],"practices,":[42],"such":[43],"as":[44],"Reward":[45,54],"Combination":[46,55,71],"Advantage":[48,70,88],"Combination,":[49],"suffer":[50],"from":[51],"significant":[52],"drawbacks:":[53],"frequently":[56],"generates":[57],"advantages":[58],"excessively":[60],"large":[61],"squared":[62],"magnitudes":[63,129],"that":[64,124,154],"lead":[65],"training":[67,132,168],"instability,":[68],"while":[69,117],"relies":[72],"on":[73,97,142],"static":[74],"hyperparameters":[75],"ignores":[77],"cross-objective":[78,137],"correlations.":[79],"To":[80],"address":[81],"these":[82],"limitations,":[83],"we":[84],"propose":[85],"Dynamic":[86],"Variance-adaptive":[87],"(DVAO),":[90],"which":[91],"dynamically":[92],"adjusts":[93],"combination":[94],"weights":[95],"based":[96],"the":[98],"empirical":[99],"reward":[100],"variance":[101],"of":[102],"each":[103],"objective":[104],"within":[105],"rollout":[107],"group,":[108],"effectively":[109],"up-weighting":[110],"objectives":[111],"stronger":[114],"learning":[115],"signal":[116],"suppressing":[118],"noisy":[119],"ones.":[120],"We":[121],"mathematically":[122],"prove":[123],"DVAO":[125,155],"maintains":[126],"bounded":[127],"advantage":[128],"stable":[131],"introduces":[134],"self-adaptive":[136],"regularization":[138],"mechanism.":[139],"Extensive":[140],"experiments":[141],"mathematical":[143],"reasoning":[144],"tool-use":[146],"benchmarks":[147],"using":[148],"Qwen3":[149],"Qwen2.5":[151],"models":[152],"demonstrate":[153],"significantly":[156],"outperforms":[157],"baseline":[158],"methods,":[159],"achieving":[160],"superior":[162],"multi-objective":[163],"Pareto":[164],"frontier":[165],"robust":[167],"stability.":[169]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-27T00:00:00"}