{"id":"https://openalex.org/W7140288970","doi":"https://doi.org/10.48550/arxiv.2603.22829","title":"Improving Safety Alignment via Balanced Direct Preference Optimization","display_name":"Improving Safety Alignment via Balanced Direct Preference Optimization","publication_year":2026,"publication_date":"2026-03-24","ids":{"openalex":"https://openalex.org/W7140288970","doi":"https://doi.org/10.48550/arxiv.2603.22829"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.22829","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.22829","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.2603.22829","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5130567514","display_name":"Shiji Zhao","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Zhao, Shiji","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130597378","display_name":"Mengyang Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Mengyang","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130626946","display_name":"Shukun Xiong","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xiong, Shukun","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130576151","display_name":"Fangzhou Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Fangzhou","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130548835","display_name":"Qihui Zhu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhu, Qihui","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5048633696","display_name":"Shouwei Ruan","orcid":"https://orcid.org/0009-0007-0481-5855"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ruan, Shouwei","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076884053","display_name":"Yisong Xiao","orcid":"https://orcid.org/0000-0001-8227-0052"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xiao, Yisong","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130549283","display_name":"Ranjie Duan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Duan, Ranjie","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130626300","display_name":"Xun Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Xun","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5130609457","display_name":"XingXing Wei","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wei, XingXing","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":10,"corresponding_author_ids":["https://openalex.org/A5130567514"],"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.2160000056028366,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.2160000056028366,"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/T10028","display_name":"Topic Modeling","score":0.19460000097751617,"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":0.10790000110864639,"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/overfitting","display_name":"Overfitting","score":0.6953999996185303},{"id":"https://openalex.org/keywords/preference","display_name":"Preference","score":0.6944000124931335},{"id":"https://openalex.org/keywords/perspective","display_name":"Perspective (graphical)","score":0.515500009059906},{"id":"https://openalex.org/keywords/comprehension","display_name":"Comprehension","score":0.38609999418258667},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.3734999895095825},{"id":"https://openalex.org/keywords/mainstream","display_name":"Mainstream","score":0.32519999146461487}],"concepts":[{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.6953999996185303},{"id":"https://openalex.org/C2781249084","wikidata":"https://www.wikidata.org/wiki/Q908656","display_name":"Preference","level":2,"score":0.6944000124931335},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5645999908447266},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.515500009059906},{"id":"https://openalex.org/C112930515","wikidata":"https://www.wikidata.org/wiki/Q4389547","display_name":"Risk analysis (engineering)","level":1,"score":0.43810001015663147},{"id":"https://openalex.org/C511192102","wikidata":"https://www.wikidata.org/wiki/Q5156948","display_name":"Comprehension","level":2,"score":0.38609999418258667},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.3734999895095825},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.34549999237060547},{"id":"https://openalex.org/C2777617010","wikidata":"https://www.wikidata.org/wiki/Q18957","display_name":"Mainstream","level":2,"score":0.32519999146461487},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3034000098705292},{"id":"https://openalex.org/C50335755","wikidata":"https://www.wikidata.org/wiki/Q483247","display_name":"Phenomenon","level":2,"score":0.3005000054836273},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.27410000562667847},{"id":"https://openalex.org/C68781425","wikidata":"https://www.wikidata.org/wiki/Q2052203","display_name":"Multi-objective optimization","level":2,"score":0.26989999413490295},{"id":"https://openalex.org/C2777632292","wikidata":"https://www.wikidata.org/wiki/Q315515","display_name":"Discretion","level":2,"score":0.26600000262260437},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.26010000705718994},{"id":"https://openalex.org/C2777868144","wikidata":"https://www.wikidata.org/wiki/Q7239817","display_name":"Preference elicitation","level":3,"score":0.25429999828338623}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.22829","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.22829","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.2603.22829","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.22829","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":[{"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions","score":0.4242175817489624}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"With":[0],"the":[1,31,70,74,77,81,87,100,138,143],"rapid":[2],"development":[3],"and":[4,39,121,165],"widespread":[5,18],"application":[6],"of":[7,34,76,80,130,147,162],"Large":[8],"Language":[9],"Models":[10],"(LLMs),":[11],"their":[12],"potential":[13],"safety":[14,32,52,55,102,139],"risks":[15],"have":[16],"attracted":[17],"attention.":[19],"Reinforcement":[20],"Learning":[21],"from":[22,59,73],"Human":[23],"Feedback":[24],"(RLHF)":[25],"has":[26],"been":[27],"adopted":[28],"to":[29,42,154],"enhance":[30,137],"performance":[33],"LLMs.":[35],"As":[36],"a":[37],"simple":[38],"effective":[40],"alternative":[41],"RLHF,":[43],"Direct":[44,110],"Preference":[45,89,111],"Optimization":[46,112],"(DPO)":[47],"is":[48,168],"widely":[49],"used":[50],"for":[51],"alignment.":[53],"However,":[54],"alignment":[56],"still":[57],"suffers":[58],"severe":[60],"overfitting,":[61],"which":[62,98,114],"limits":[63],"its":[64],"actual":[65],"performance.":[66,103],"This":[67,158],"paper":[68,159],"revisits":[69],"overfitting":[71],"phenomenon":[72,91],"perspective":[75],"model's":[78,101],"comprehension":[79],"training":[82],"data.":[83],"We":[84],"find":[85],"that":[86,134],"Imbalanced":[88],"Comprehension":[90],"exists":[92],"between":[93,119],"responses":[94,123],"in":[95],"preference":[96],"pairs,":[97],"compromises":[99],"To":[104],"address":[105],"this,":[106],"we":[107],"propose":[108],"Balanced":[109],"(B-DPO),":[113],"adaptively":[115],"modulates":[116],"optimization":[117],"strength":[118],"preferred":[120],"dispreferred":[122],"based":[124],"on":[125,149],"mutual":[126],"information.":[127],"A":[128],"series":[129],"experimental":[131],"results":[132],"show":[133],"B-DPO":[135],"can":[136],"capability":[140],"while":[141],"maintaining":[142],"competitive":[144],"general":[145],"capabilities":[146],"LLMs":[148],"various":[150],"mainstream":[151],"benchmarks":[152],"compared":[153],"state-of-the-art":[155],"methods.":[156],"\\color{red}{Warning:":[157],"contains":[160],"examples":[161],"harmful":[163],"texts,":[164],"reader":[166],"discretion":[167],"recommended.":[169]},"counts_by_year":[],"updated_date":"2026-03-26T06:10:45.909354","created_date":"2026-03-26T00:00:00"}
