{"id":"https://openalex.org/W4417530942","doi":"https://doi.org/10.48550/arxiv.2512.16626","title":"Stackelberg Learning from Human Feedback: Preference Optimization as a Sequential Game","display_name":"Stackelberg Learning from Human Feedback: Preference Optimization as a Sequential Game","publication_year":2025,"publication_date":"2025-12-18","ids":{"openalex":"https://openalex.org/W4417530942","doi":"https://doi.org/10.48550/arxiv.2512.16626"},"language":null,"primary_location":{"id":"pmh:oai:arXiv.org:2512.16626","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2512.16626","pdf_url":"https://arxiv.org/pdf/2512.16626","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2512.16626","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"P\u00e1sztor, Barna","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"P\u00e1sztor, Barna","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Buening, Thomas Kleine","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Buening, Thomas Kleine","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":null,"display_name":"Krause, Andreas","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Krause, Andreas","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"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":0.1492999941110611,"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":0.1492999941110611,"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/T10203","display_name":"Recommender Systems and Techniques","score":0.09149999916553497,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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":0.08540000021457672,"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/stackelberg-competition","display_name":"Stackelberg competition","score":0.6883000135421753},{"id":"https://openalex.org/keywords/preference-learning","display_name":"Preference learning","score":0.6621000170707703},{"id":"https://openalex.org/keywords/preference","display_name":"Preference","score":0.5611000061035156},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.541100025177002},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.4399000108242035},{"id":"https://openalex.org/keywords/adversary","display_name":"Adversary","score":0.4018000066280365},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.3885999917984009},{"id":"https://openalex.org/keywords/optimization-problem","display_name":"Optimization problem","score":0.35260000824928284}],"concepts":[{"id":"https://openalex.org/C199510392","wikidata":"https://www.wikidata.org/wiki/Q1184602","display_name":"Stackelberg competition","level":2,"score":0.6883000135421753},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6671000123023987},{"id":"https://openalex.org/C181204326","wikidata":"https://www.wikidata.org/wiki/Q7239820","display_name":"Preference learning","level":3,"score":0.6621000170707703},{"id":"https://openalex.org/C2781249084","wikidata":"https://www.wikidata.org/wiki/Q908656","display_name":"Preference","level":2,"score":0.5611000061035156},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.541100025177002},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4693000018596649},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.4399000108242035},{"id":"https://openalex.org/C41065033","wikidata":"https://www.wikidata.org/wiki/Q2825412","display_name":"Adversary","level":2,"score":0.4018000066280365},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.398499995470047},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.3885999917984009},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.35260000824928284},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.349700003862381},{"id":"https://openalex.org/C106189395","wikidata":"https://www.wikidata.org/wiki/Q176789","display_name":"Markov decision process","level":3,"score":0.3359000086784363},{"id":"https://openalex.org/C117619785","wikidata":"https://www.wikidata.org/wiki/Q6094414","display_name":"Iterative learning control","level":3,"score":0.322299987077713},{"id":"https://openalex.org/C2777868144","wikidata":"https://www.wikidata.org/wiki/Q7239817","display_name":"Preference elicitation","level":3,"score":0.31459999084472656},{"id":"https://openalex.org/C90329073","wikidata":"https://www.wikidata.org/wiki/Q914232","display_name":"Ask price","level":2,"score":0.30979999899864197},{"id":"https://openalex.org/C46814582","wikidata":"https://www.wikidata.org/wiki/Q23389","display_name":"Nash equilibrium","level":2,"score":0.30889999866485596},{"id":"https://openalex.org/C22171661","wikidata":"https://www.wikidata.org/wiki/Q1074380","display_name":"Stochastic game","level":2,"score":0.3073999881744385},{"id":"https://openalex.org/C145071142","wikidata":"https://www.wikidata.org/wiki/Q1411116","display_name":"Fictitious play","level":3,"score":0.29910001158714294},{"id":"https://openalex.org/C77967617","wikidata":"https://www.wikidata.org/wiki/Q4677561","display_name":"Active learning (machine learning)","level":2,"score":0.26980000734329224},{"id":"https://openalex.org/C177142836","wikidata":"https://www.wikidata.org/wiki/Q44455","display_name":"Game theory","level":2,"score":0.2660999894142151},{"id":"https://openalex.org/C40506919","wikidata":"https://www.wikidata.org/wiki/Q7452469","display_name":"Sequence learning","level":2,"score":0.26330000162124634},{"id":"https://openalex.org/C2781043087","wikidata":"https://www.wikidata.org/wiki/Q939761","display_name":"Preference theory","level":3,"score":0.2596000134944916}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2512.16626","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2512.16626","pdf_url":"https://arxiv.org/pdf/2512.16626","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2512.16626","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2512.16626","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":"pmh:oai:arXiv.org:2512.16626","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2512.16626","pdf_url":"https://arxiv.org/pdf/2512.16626","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"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":{"We":[0,129],"introduce":[1],"Stackelberg":[2],"Learning":[3,67,80],"from":[4,68,81,169],"Human":[5,69,82],"Feedback":[6,70,83],"(SLHF),":[7],"a":[8,20,26,34,49,87],"new":[9],"framework":[10],"for":[11,52,62],"preference":[12,46,100,166],"optimization.":[13],"SLHF":[14,90,106,160],"frames":[15],"the":[16,40,53,63,92,112,117,131],"alignment":[17,163],"problem":[18,51,58],"as":[19,111],"sequential-move":[21],"game":[22],"between":[23],"two":[24],"policies:":[25],"Leader,":[27],"which":[28,36,72,85],"commits":[29],"to":[30,76,97,115,150,171],"an":[31,56,60],"action,":[32],"and":[33,55,120,137,139,148,174],"Follower,":[35],"responds":[37],"conditionally":[38],"on":[39,154],"Leader's":[41,118],"action.":[42],"This":[43],"approach":[44],"decomposes":[45],"optimization":[47,57],"into":[48],"refinement":[50],"Follower":[54,113],"against":[59],"adversary":[61],"Leader.":[64],"Unlike":[65],"Reinforcement":[66],"(RLHF),":[71],"assigns":[73],"scalar":[74],"rewards":[75],"actions,":[77,119],"or":[78],"Nash":[79],"(NLHF),":[84],"seeks":[86],"simultaneous-move":[88],"equilibrium,":[89],"leverages":[91],"asymmetry":[93],"of":[94,105,134],"sequential":[95,103],"play":[96],"capture":[98],"richer":[99],"structures.":[101],"The":[102],"design":[104],"naturally":[107],"enables":[108],"inference-time":[109,176],"refinement,":[110],"learns":[114],"improve":[116],"these":[121],"refinements":[122,177],"can":[123],"be":[124],"leveraged":[125],"through":[126],"iterative":[127],"sampling.":[128],"compare":[130],"solution":[132],"concepts":[133],"SLHF,":[135],"RLHF,":[136],"NLHF,":[138],"lay":[140],"out":[141],"key":[142],"advantages":[143],"in":[144],"consistency,":[145],"data":[146],"sensitivity,":[147],"robustness":[149],"intransitive":[151],"preferences.":[152],"Experiments":[153],"large":[155],"language":[156],"models":[157],"demonstrate":[158],"that":[159,178],"achieves":[161],"strong":[162],"across":[164,180],"diverse":[165],"datasets,":[167],"scales":[168],"0.5B":[170],"8B":[172],"parameters,":[173],"yields":[175],"transfer":[179],"model":[181],"families":[182],"without":[183],"further":[184],"fine-tuning.":[185]},"counts_by_year":[],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-12-21T00:00:00"}
