{"id":"https://openalex.org/W7127988493","doi":"https://doi.org/10.48550/arxiv.2602.05993","title":"Diamond Maps: Efficient Reward Alignment via Stochastic Flow Maps","display_name":"Diamond Maps: Efficient Reward Alignment via Stochastic Flow Maps","publication_year":2026,"publication_date":"2026-02-05","ids":{"openalex":"https://openalex.org/W7127988493","doi":"https://doi.org/10.48550/arxiv.2602.05993"},"language":null,"primary_location":{"id":"pmh:doi:10.48550/arxiv.2602.05993","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"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":"Article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":null,"any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5053763440","display_name":"Peter Holderrieth","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Holderrieth, Peter","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5125238147","display_name":"Douglas Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Douglas","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5052657819","display_name":"Luca Eyring","orcid":"https://orcid.org/0000-0002-2770-2542"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Eyring, Luca","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5125226608","display_name":"Ishin Shah","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shah, Ishin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5080643211","display_name":"Giri Anantharaman","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Anantharaman, Giri","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102289110","display_name":"Yutong He","orcid":"https://orcid.org/0009-0002-9560-3113"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"He, Yutong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5120644176","display_name":"Zeynep Akata","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Akata, Zeynep","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5048915657","display_name":"Tommi Jaakkola","orcid":"https://orcid.org/0000-0002-2199-0379"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jaakkola, Tommi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054972412","display_name":"Nicholas M. Boffi","orcid":"https://orcid.org/0000-0003-1336-7568"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Boffi, Nicholas Matthew","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5037154191","display_name":"Max Simchowitz","orcid":"https://orcid.org/0000-0001-9900-1238"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Simchowitz, Max","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":10,"corresponding_author_ids":["https://openalex.org/A5053763440"],"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.5088000297546387,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.5088000297546387,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T13702","display_name":"Machine Learning in Healthcare","score":0.05220000073313713,"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":0.04830000177025795,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6542999744415283},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.5942000150680542},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.48840001225471497},{"id":"https://openalex.org/keywords/flow","display_name":"Flow (mathematics)","score":0.4855000078678131},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.45980000495910645},{"id":"https://openalex.org/keywords/property","display_name":"Property (philosophy)","score":0.42910000681877136},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.4156999886035919},{"id":"https://openalex.org/keywords/workflow","display_name":"Workflow","score":0.36959999799728394}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6905999779701233},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6542999744415283},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.5942000150680542},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.48840001225471497},{"id":"https://openalex.org/C38349280","wikidata":"https://www.wikidata.org/wiki/Q1434290","display_name":"Flow (mathematics)","level":2,"score":0.4855000078678131},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.45980000495910645},{"id":"https://openalex.org/C189950617","wikidata":"https://www.wikidata.org/wiki/Q937228","display_name":"Property (philosophy)","level":2,"score":0.42910000681877136},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.4156999886035919},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3822000026702881},{"id":"https://openalex.org/C177212765","wikidata":"https://www.wikidata.org/wiki/Q627335","display_name":"Workflow","level":2,"score":0.36959999799728394},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.35740000009536743},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.33180001378059387},{"id":"https://openalex.org/C50494287","wikidata":"https://www.wikidata.org/wiki/Q658467","display_name":"Texture synthesis","level":5,"score":0.32850000262260437},{"id":"https://openalex.org/C88871306","wikidata":"https://www.wikidata.org/wiki/Q7208287","display_name":"Point process","level":2,"score":0.31299999356269836},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.30250000953674316},{"id":"https://openalex.org/C2776291640","wikidata":"https://www.wikidata.org/wiki/Q2912517","display_name":"Value (mathematics)","level":2,"score":0.30149999260902405},{"id":"https://openalex.org/C134261354","wikidata":"https://www.wikidata.org/wiki/Q938438","display_name":"Statistical inference","level":2,"score":0.2946000099182129},{"id":"https://openalex.org/C127491075","wikidata":"https://www.wikidata.org/wiki/Q7617825","display_name":"Stochastic modelling","level":2,"score":0.28769999742507935},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2777999937534332},{"id":"https://openalex.org/C61445026","wikidata":"https://www.wikidata.org/wiki/Q217608","display_name":"Fixed point","level":2,"score":0.2736999988555908},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.27129998803138733},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.27059999108314514},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.25690001249313354},{"id":"https://openalex.org/C8272713","wikidata":"https://www.wikidata.org/wiki/Q176737","display_name":"Stochastic process","level":2,"score":0.25529998540878296}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:doi:10.48550/arxiv.2602.05993","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"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":"Article"},{"id":"doi:10.48550/arxiv.2602.05993","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.05993","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":"article"}],"best_oa_location":{"id":"pmh:doi:10.48550/arxiv.2602.05993","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"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":"Article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Flow":[0],"and":[1,18,44,61,99,105,132,156],"diffusion":[2],"models":[3,57,147],"produce":[4],"high-quality":[5],"samples,":[6],"but":[7],"adapting":[8],"them":[9],"to":[10,64,145,153],"user":[11],"preferences":[12,155],"or":[13],"constraints":[14,157],"post-training":[15],"remains":[16],"costly":[17],"brittle,":[19],"a":[20,34,77,142],"challenge":[21],"commonly":[22],"called":[23],"reward":[24,30,90,129],"alignment.":[25,91],"We":[26,50],"argue":[27],"that":[28,58,115,148],"efficient":[29,60,104],"alignment":[31,63,130],"should":[32],"be":[33,119,150],"property":[35],"of":[36,108],"the":[37,46,85,109],"generative":[38,146],"model":[39,47],"itself,":[40],"not":[41],"an":[42],"afterthought,":[43],"redesign":[45],"for":[48,88],"adaptability.":[49],"propose":[51],"\"Diamond":[52],"Maps\",":[53],"stochastic":[54],"flow":[55,81],"map":[56],"enable":[59],"accurate":[62],"arbitrary":[65,154],"rewards":[66],"at":[67,158],"inference":[68,159],"time.":[69,160],"Diamond":[70,116],"Maps":[71,117],"amortize":[72],"many":[73],"simulation":[74],"steps":[75],"into":[76],"single-step":[78],"sampler,":[79],"like":[80],"maps,":[82],"while":[83],"preserving":[84],"stochasticity":[86],"required":[87],"optimal":[89],"This":[92],"design":[93],"makes":[94],"search,":[95],"Sequential":[96],"Monte":[97],"Carlo,":[98],"guidance":[100],"scalable":[101],"by":[102],"enabling":[103],"consistent":[106],"estimation":[107],"value":[110],"function.":[111],"Our":[112,138],"experiments":[113],"show":[114],"can":[118,149],"learned":[120],"efficiently":[121],"via":[122],"distillation":[123],"from":[124],"GLASS":[125],"Flows,":[126],"achieve":[127],"stronger":[128],"performance,":[131],"scale":[133],"better":[134],"than":[135],"existing":[136],"methods.":[137],"results":[139],"point":[140],"toward":[141],"practical":[143],"route":[144],"rapidly":[151],"adapted":[152]},"counts_by_year":[],"updated_date":"2026-05-04T08:30:34.212998","created_date":"2026-02-07T00:00:00"}
