{"id":"https://openalex.org/W7161712099","doi":"https://doi.org/10.48550/arxiv.2605.18191","title":"Pairwise Preference Reward and Group-Based Diversity Enhancement for Superior Open-Ended Generation","display_name":"Pairwise Preference Reward and Group-Based Diversity Enhancement for Superior Open-Ended Generation","publication_year":2026,"publication_date":"2026-05-18","ids":{"openalex":"https://openalex.org/W7161712099","doi":"https://doi.org/10.48550/arxiv.2605.18191"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.18191","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.18191","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.2605.18191","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5136471644","display_name":"Guining Cao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cao, Guining","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136488568","display_name":"Jiaxin Peng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Peng, Jiaxin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136471273","display_name":"Chu Zeng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zeng, Chu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136468747","display_name":"Yu Zhao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhao, Yu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136462199","display_name":"Shuangyong Song","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Song, Shuangyong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5136483979","display_name":"Yongxiang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yongxiang","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":0.5580000281333923,"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":0.5580000281333923,"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/T10883","display_name":"Ethics and Social Impacts of AI","score":0.03610000014305115,"subfield":{"id":"https://openalex.org/subfields/3311","display_name":"Safety Research"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.03269999846816063,"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/pairwise-comparison","display_name":"Pairwise comparison","score":0.7615000009536743},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.5767999887466431},{"id":"https://openalex.org/keywords/preference","display_name":"Preference","score":0.5450999736785889},{"id":"https://openalex.org/keywords/diversity","display_name":"Diversity (politics)","score":0.5157999992370605},{"id":"https://openalex.org/keywords/correctness","display_name":"Correctness","score":0.4551999866962433},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.43939998745918274},{"id":"https://openalex.org/keywords/reinforcement","display_name":"Reinforcement","score":0.37529999017715454}],"concepts":[{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.7615000009536743},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.597599983215332},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.5767999887466431},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5490000247955322},{"id":"https://openalex.org/C2781249084","wikidata":"https://www.wikidata.org/wiki/Q908656","display_name":"Preference","level":2,"score":0.5450999736785889},{"id":"https://openalex.org/C2781316041","wikidata":"https://www.wikidata.org/wiki/Q1230584","display_name":"Diversity (politics)","level":2,"score":0.5157999992370605},{"id":"https://openalex.org/C55439883","wikidata":"https://www.wikidata.org/wiki/Q360812","display_name":"Correctness","level":2,"score":0.4551999866962433},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.43939998745918274},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4302999973297119},{"id":"https://openalex.org/C67203356","wikidata":"https://www.wikidata.org/wiki/Q1321905","display_name":"Reinforcement","level":2,"score":0.37529999017715454},{"id":"https://openalex.org/C181204326","wikidata":"https://www.wikidata.org/wiki/Q7239820","display_name":"Preference learning","level":3,"score":0.3686000108718872},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.32420000433921814},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.2827000021934509},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2662000060081482},{"id":"https://openalex.org/C198082294","wikidata":"https://www.wikidata.org/wiki/Q3399648","display_name":"Position (finance)","level":2,"score":0.26179999113082886},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.2605000138282776},{"id":"https://openalex.org/C2776493592","wikidata":"https://www.wikidata.org/wiki/Q5158717","display_name":"Succinctness","level":2,"score":0.250900000333786}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.18191","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.18191","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.2605.18191","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.18191","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":{"Current":[0],"reinforcement":[1,41],"learning(RL)":[2],"methods":[3],"are":[4,57,141],"broadly":[5],"applicable":[6],"and":[7,30,37,49,68,89,121,201],"powerful":[8],"in":[9,19,60,185,196],"verifiable":[10],"settings":[11],"where":[12],"scalar":[13,87],"rewards":[14,88],"can":[15],"be":[16],"provided.":[17],"However,":[18],"open-ended":[20,81],"generation":[21],"tasks,":[22],"verifying":[23],"the":[24,94,99,189],"correctness":[25],"of":[26,102,137],"responses":[27],"remains":[28],"challenging,":[29],"training":[31],"reward":[32,95,126,139],"models":[33],"incurs":[34],"substantial":[35],"computational":[36],"annotation":[38],"costs.":[39],"Moreover,":[40],"learning":[42],"(RLVR)":[43],"often":[44],"leads":[45],"to":[46],"diversity":[47,92,125,169,190,200],"collapse":[48],"produces":[50],"stereotypical":[51],"or":[52],"rigid":[53],"outputs,":[54],"outcomes":[55],"that":[56,76,127,158,177],"particularly":[58],"undesirable":[59],"open-domain":[61],"scenarios.":[62],"We":[63,150],"propose":[64],"Pairwise":[65],"Preference":[66],"Reward":[67],"Group-based":[69],"Diversity":[70],"Enhancement":[71],"(PPR-GDE),":[72],"a":[73,106,123,133,144,161],"RL":[74,172],"method":[75],"is":[77,180],"more":[78],"suitable":[79],"for":[80,182],"generation.":[82],"PPR-GDE":[83,152,159],"does":[84],"not":[85],"require":[86],"incorporates":[90],"group-level":[91],"into":[93,143],"signal,":[96],"it":[97],"preserves":[98],"comparative":[100],"structure":[101],"subjective":[103,186],"evaluation":[104],"through":[105],"pairwise":[107,178],"preference":[108,179,183],"reward,":[109],"mitigates":[110],"judge":[111],"position":[112],"bias":[113],"via":[114],"repeated":[115],"comparisons":[116],"with":[117],"swapped":[118],"response":[119,134],"order,":[120],"introduces":[122],"group-based":[124],"explicitly":[128],"encourages":[129],"semantic":[130,203],"dispersion":[131],"within":[132],"group,":[135],"all":[136],"these":[138],"signals":[140],"integrated":[142],"unified":[145],"group-relative":[146],"policy":[147],"optimization":[148],"objective.":[149],"instantiate":[151],"on":[153],"role-playing":[154],"task,":[155],"experiments":[156],"show":[157],"achieves":[160],"better":[162],"alignment":[163,184],"quality":[164],"as":[165,167],"well":[166],"expressive":[168,199],"than":[170],"strong":[171],"baselines.":[173],"Further":[174],"analysis":[175],"shows":[176],"critical":[181],"perspective,":[187],"while":[188],"metric":[191],"plays":[192],"an":[193],"essential":[194],"role":[195],"achieving":[197],"superior":[198],"broader":[202],"coverage.":[204]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-20T00:00:00"}