{"id":"https://openalex.org/W4417296363","doi":"https://doi.org/10.48550/arxiv.2512.10601","title":"Multi-Objective Reward and Preference Optimization: Theory and Algorithms","display_name":"Multi-Objective Reward and Preference Optimization: Theory and Algorithms","publication_year":2025,"publication_date":"2025-12-11","ids":{"openalex":"https://openalex.org/W4417296363","doi":"https://doi.org/10.48550/arxiv.2512.10601"},"language":null,"primary_location":{"id":"pmh:oai:arXiv.org:2512.10601","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2512.10601","pdf_url":"https://arxiv.org/pdf/2512.10601","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":"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/2512.10601","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5015918255","display_name":"Akhil Agnihotri","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Agnihotri, Akhil","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":["https://openalex.org/A5015918255"],"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":0.6545000076293945,"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":0.6545000076293945,"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/T12101","display_name":"Advanced Bandit Algorithms Research","score":0.12919999659061432,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.018799999728798866,"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/markov-decision-process","display_name":"Markov decision process","score":0.6425999999046326},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.6290000081062317},{"id":"https://openalex.org/keywords/regret","display_name":"Regret","score":0.5873000025749207},{"id":"https://openalex.org/keywords/pairwise-comparison","display_name":"Pairwise comparison","score":0.4875999987125397},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.4027000069618225},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.39750000834465027},{"id":"https://openalex.org/keywords/thompson-sampling","display_name":"Thompson sampling","score":0.3785000145435333},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.3772999942302704},{"id":"https://openalex.org/keywords/preference-elicitation","display_name":"Preference elicitation","score":0.37119999527931213},{"id":"https://openalex.org/keywords/constrained-optimization","display_name":"Constrained optimization","score":0.334199994802475}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7150999903678894},{"id":"https://openalex.org/C106189395","wikidata":"https://www.wikidata.org/wiki/Q176789","display_name":"Markov decision process","level":3,"score":0.6425999999046326},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.6290000081062317},{"id":"https://openalex.org/C50817715","wikidata":"https://www.wikidata.org/wiki/Q79895177","display_name":"Regret","level":2,"score":0.5873000025749207},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5001999735832214},{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.4875999987125397},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4862000048160553},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.4027000069618225},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.39750000834465027},{"id":"https://openalex.org/C73602740","wikidata":"https://www.wikidata.org/wiki/Q7795822","display_name":"Thompson sampling","level":3,"score":0.3785000145435333},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.3772999942302704},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3716999888420105},{"id":"https://openalex.org/C2777868144","wikidata":"https://www.wikidata.org/wiki/Q7239817","display_name":"Preference elicitation","level":3,"score":0.37119999527931213},{"id":"https://openalex.org/C55660270","wikidata":"https://www.wikidata.org/wiki/Q5164377","display_name":"Constrained optimization","level":2,"score":0.334199994802475},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.3228999972343445},{"id":"https://openalex.org/C2776036281","wikidata":"https://www.wikidata.org/wiki/Q48769818","display_name":"Constraint (computer-aided design)","level":2,"score":0.32280001044273376},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.3127000033855438},{"id":"https://openalex.org/C181204326","wikidata":"https://www.wikidata.org/wiki/Q7239820","display_name":"Preference learning","level":3,"score":0.3012000024318695},{"id":"https://openalex.org/C44616089","wikidata":"https://www.wikidata.org/wiki/Q30158686","display_name":"Constraint satisfaction","level":3,"score":0.2996000051498413},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2994000017642975},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.296099990606308},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.29499998688697815},{"id":"https://openalex.org/C2781249084","wikidata":"https://www.wikidata.org/wiki/Q908656","display_name":"Preference","level":2,"score":0.289900004863739},{"id":"https://openalex.org/C120936955","wikidata":"https://www.wikidata.org/wiki/Q2155640","display_name":"Empirical research","level":2,"score":0.2791999876499176},{"id":"https://openalex.org/C159886148","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov process","level":2,"score":0.27799999713897705},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2759999930858612},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.2720000147819519},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.26919999718666077},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.2687999904155731},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.2669000029563904},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.2590999901294708},{"id":"https://openalex.org/C133462117","wikidata":"https://www.wikidata.org/wiki/Q4929239","display_name":"Data collection","level":2,"score":0.25839999318122864},{"id":"https://openalex.org/C41045048","wikidata":"https://www.wikidata.org/wiki/Q202843","display_name":"Linear programming","level":2,"score":0.2583000063896179}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2512.10601","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2512.10601","pdf_url":"https://arxiv.org/pdf/2512.10601","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2512.10601","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2512.10601","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":"pmh:oai:arXiv.org:2512.10601","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2512.10601","pdf_url":"https://arxiv.org/pdf/2512.10601","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":"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":{"This":[0],"thesis":[1,98,209],"develops":[2],"theoretical":[3,60,220],"frameworks":[4],"and":[5,17,92,135,154,165,201,216,222,227],"algorithms":[6],"that":[7,115,195],"advance":[8],"constrained":[9,27,184,211],"reinforcement":[10,101],"learning":[11,102],"(RL)":[12],"across":[13,204,213],"control,":[14],"preference":[15,118],"learning,":[16],"alignment":[18,205],"of":[19,128,169],"large":[20],"language":[21,199],"models.":[22],"The":[23,97,142,172],"first":[24,73],"contribution":[25,174],"addresses":[26],"Markov":[28],"Decision":[29],"Processes":[30],"(CMDPs)":[31],"under":[32],"the":[33,37,72,208],"average-cost":[34],"criterion":[35],"through":[36],"Average-Constrained":[38],"Policy":[39],"Optimization":[40],"(ACPO)":[41],"algorithm.":[42],"ACPO":[43],"integrates":[44,116],"sensitivity":[45],"analysis":[46],"with":[47,59,192],"trust-region":[48],"updates":[49,194],"to":[50,67,178,197],"ensure":[51],"stable":[52],"constraint":[53],"handling,":[54],"achieving":[55],"state-of-the-art":[56],"empirical":[57,167],"performance":[58],"guarantees.":[61],"Constrained":[62],"RL":[63,148,212],"is":[64],"then":[65,99],"extended":[66],"finite-horizon":[68],"settings":[69],"via":[70],"e-COP,":[71],"policy":[74,84],"optimization":[75,185],"method":[76],"for":[77,112,140,225],"episodic":[78,83],"CMDPs.":[79],"Built":[80],"on":[81],"an":[82,189],"difference":[85],"lemma,":[86],"e-COP":[87],"offers":[88],"provable":[89],"performance,":[90],"simplicity,":[91],"scalability":[93],"in":[94],"safety-critical":[95],"environments.":[96],"investigates":[100],"from":[103,120,157],"human":[104],"preferences.":[105],"warmPref-PS":[106],"introduces":[107],"a":[108],"posterior":[109],"sampling":[110,151],"strategy":[111],"linear":[113],"bandits":[114],"offline":[117],"data":[119,138],"heterogeneous":[121],"raters":[122],"into":[123],"online":[124],"learning.":[125],"Explicit":[126],"modeling":[127],"rater":[129],"competence":[130],"yields":[131,187],"substantial":[132],"regret":[133],"reduction":[134],"more":[136],"efficient":[137],"collection":[139],"RLHF.":[141],"PSPL":[143],"algorithm":[144,191],"further":[145],"advances":[146,221],"preference-based":[147],"by":[149],"jointly":[150],"reward":[152],"models":[153,200],"transition":[155],"dynamics":[156],"pairwise":[158],"trajectory":[159],"comparisons,":[160],"providing":[161],"Bayesian":[162],"simple-regret":[163],"guarantees":[164],"robust":[166,203],"identification":[168],"optimal":[170],"policies.":[171],"final":[173],"applies":[175],"these":[176],"methods":[177],"large-scale":[179],"model":[180],"alignment.":[181],"A":[182],"multi-objective":[183],"view":[186],"MOPO,":[188],"iterative":[190],"closed-form":[193],"scales":[196],"multi-billion-parameter":[198],"remains":[202],"settings.":[206],"Collectively,":[207],"unifies":[210],"average-cost,":[214],"episodic,":[215],"preference-driven":[217],"paradigms,":[218],"delivering":[219],"practical":[223],"tools":[224],"safe":[226],"aligned":[228],"decision-making.":[229]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2025-12-13T00:00:00"}