{"id":"https://openalex.org/W7163193612","doi":"https://doi.org/10.48550/arxiv.2606.01561","title":"S-SPPO: Semantic-Calibrated Self-Play Preference Optimization","display_name":"S-SPPO: Semantic-Calibrated Self-Play Preference Optimization","publication_year":2026,"publication_date":"2026-06-01","ids":{"openalex":"https://openalex.org/W7163193612","doi":"https://doi.org/10.48550/arxiv.2606.01561"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.01561","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.01561","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.2606.01561","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100598315","display_name":"Chen Xiwen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Xiwen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100439574","display_name":"Wenhui Zhu","orcid":"https://orcid.org/0009-0000-5207-6283"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhu, Wenhui","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137682828","display_name":"Jingjing Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Jingjing","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058590658","display_name":"Peijie Qiu","orcid":"https://orcid.org/0000-0002-1591-5436"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Qiu, Peijie","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137680653","display_name":"Zhipeng Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Zhipeng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137712746","display_name":"Huayu Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Huayu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137638533","display_name":"ZhengXiao He","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"He, ZhengXiao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111345742","display_name":"Xuanzhao Dong","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dong, Xuanzhao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137658632","display_name":"Prayag Tiwari","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tiwari, Prayag","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137665300","display_name":"Mingkun Xu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xu, Mingkun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137693699","display_name":"Yujian Xiong","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xiong, Yujian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137644171","display_name":"Feng Luo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Luo, Feng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137625393","display_name":"Abolfazl Razi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Razi, Abolfazl","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033834115","display_name":"Brendan Rappazzo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rappazzo, Brendan Hogan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137671779","display_name":"Anderson Schneider","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Schneider, Anderson","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5081397604","display_name":"Yuriy Nevmyvaka","orcid":"https://orcid.org/0009-0001-3484-7483"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nevmyvaka, Yuriy","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":0.19850000739097595,"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":0.19850000739097595,"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/T11574","display_name":"Artificial Intelligence in Games","score":0.09589999914169312,"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/T11704","display_name":"Mobile Crowdsensing and Crowdsourcing","score":0.07800000160932541,"subfield":{"id":"https://openalex.org/subfields/1706","display_name":"Computer Science Applications"},"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/oracle","display_name":"Oracle","score":0.5971999764442444},{"id":"https://openalex.org/keywords/transitive-relation","display_name":"Transitive relation","score":0.5952000021934509},{"id":"https://openalex.org/keywords/preference","display_name":"Preference","score":0.5794000029563904},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5234000086784363},{"id":"https://openalex.org/keywords/calibration","display_name":"Calibration","score":0.4846000075340271},{"id":"https://openalex.org/keywords/baseline","display_name":"Baseline (sea)","score":0.47780001163482666},{"id":"https://openalex.org/keywords/convergence","display_name":"Convergence (economics)","score":0.3785000145435333},{"id":"https://openalex.org/keywords/rate-of-convergence","display_name":"Rate of convergence","score":0.37709999084472656}],"concepts":[{"id":"https://openalex.org/C55166926","wikidata":"https://www.wikidata.org/wiki/Q2892946","display_name":"Oracle","level":2,"score":0.5971999764442444},{"id":"https://openalex.org/C191399111","wikidata":"https://www.wikidata.org/wiki/Q64861","display_name":"Transitive relation","level":2,"score":0.5952000021934509},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5878000259399414},{"id":"https://openalex.org/C2781249084","wikidata":"https://www.wikidata.org/wiki/Q908656","display_name":"Preference","level":2,"score":0.5794000029563904},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5234000086784363},{"id":"https://openalex.org/C165838908","wikidata":"https://www.wikidata.org/wiki/Q736777","display_name":"Calibration","level":2,"score":0.4846000075340271},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.47780001163482666},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.4551999866962433},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.44339999556541443},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4036000072956085},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.3785000145435333},{"id":"https://openalex.org/C57869625","wikidata":"https://www.wikidata.org/wiki/Q1783502","display_name":"Rate of convergence","level":3,"score":0.37709999084472656},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.3763999938964844},{"id":"https://openalex.org/C170133592","wikidata":"https://www.wikidata.org/wiki/Q1806883","display_name":"Latent semantic analysis","level":2,"score":0.33219999074935913},{"id":"https://openalex.org/C46814582","wikidata":"https://www.wikidata.org/wiki/Q23389","display_name":"Nash equilibrium","level":2,"score":0.3156000077724457},{"id":"https://openalex.org/C193254401","wikidata":"https://www.wikidata.org/wiki/Q2160088","display_name":"Robust optimization","level":2,"score":0.3021000027656555},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.29510000348091125},{"id":"https://openalex.org/C2781316041","wikidata":"https://www.wikidata.org/wiki/Q1230584","display_name":"Diversity (politics)","level":2,"score":0.2818000018596649},{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.2808000147342682},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2797999978065491},{"id":"https://openalex.org/C2777868144","wikidata":"https://www.wikidata.org/wiki/Q7239817","display_name":"Preference elicitation","level":3,"score":0.2727000117301941},{"id":"https://openalex.org/C149672232","wikidata":"https://www.wikidata.org/wiki/Q337048","display_name":"Adaptive optimization","level":2,"score":0.2678999900817871},{"id":"https://openalex.org/C16345878","wikidata":"https://www.wikidata.org/wiki/Q107472979","display_name":"Orientation (vector space)","level":2,"score":0.2515999972820282}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.01561","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.01561","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.2606.01561","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.01561","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":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Aligning":[0],"Large":[1],"Language":[2],"Models":[3],"(LLMs)":[4],"with":[5,178],"human":[6,32],"preferences":[7,184],"is":[8,23,67],"often":[9],"formulated":[10],"via":[11,99,119],"Direct":[12],"Preference":[13,42],"Optimization":[14,43],"(DPO).":[15],"However,":[16],"the":[17,48,65,73,108,143,146,159],"standard":[18],"Bradley-Terry":[19],"instantiation":[20],"of":[21],"DPO":[22],"limited":[24],"in":[25,31,63,163],"modeling":[26],"common":[27],"departures":[28],"from":[29],"transitivity":[30],"preferences.":[33],"To":[34,84],"address":[35],"this,":[36,86],"recent":[37],"work":[38],"has":[39],"introduced":[40],"Self-Play":[41],"(SPPO),":[44],"which":[45,102],"iteratively":[46],"refines":[47],"policy":[49,70],"by":[50],"training":[51],"on":[52,175],"self-generated":[53],"win-lose":[54],"pairs.":[55],"Our":[56],"investigation,":[57],"however,":[58],"reveals":[59],"a":[60,90,153],"critical":[61],"instability":[62],"SPPO:":[64],"optimization":[66],"prone":[68],"to":[69,80,122,126,152],"degeneration":[71],"when":[72],"preference":[74],"oracle":[75],"assigns":[76],"overly":[77],"confident":[78],"wins":[79],"semantically":[81],"indistinguishable":[82],"responses.":[83],"mitigate":[85],"we":[87,140],"propose":[88],"S-SPPO,":[89],"dual-space":[91],"semantic":[92,100,112],"calibration":[93,144],"framework":[94],"comprising:":[95],"i)":[96],"Supervision":[97],"Calibration":[98,118],"gating,":[101],"anneals":[103],"win":[104,168,173],"rate":[105,169,174],"targets":[106],"toward":[107],"maximum-entropy":[109],"baseline":[110],"as":[111],"overlap":[113],"increases;":[114],"and":[115,130,136,170],"ii)":[116],"Representation":[117],"latent":[120,132],"repulsion":[121],"enforce":[123],"geometric":[124],"diversity":[125,133],"prevent":[127],"manifold":[128],"collapse":[129],"maintain":[131],"between":[134],"chosen":[135],"rejected":[137],"samples.":[138],"Theoretically,":[139],"show":[141],"that":[142],"preserves":[145],"constant-sum":[147],"game":[148],"structure,":[149],"facilitating":[150],"convergence":[151],"Nash":[154],"Equilibrium.":[155],"Empirically,":[156],"S-SPPO":[157],"avoids":[158],"performance":[160],"degradation":[161],"seen":[162],"prior":[164],"methods,":[165],"achieving":[166],"52.19%":[167],"47.46%":[171],"length-controlled":[172],"AlpacaEval":[176],"2.0":[177],"Llama-3-8B,":[179],"without":[180],"using":[181],"additional":[182],"human-annotated":[183],"during":[185],"training.":[186],"The":[187],"code":[188],"will":[189],"be":[190],"available":[191],"at":[192],"https://github.com/xiwenc1/s-sppo.":[193]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-03T00:00:00"}