{"id":"https://openalex.org/W7165619952","doi":"https://doi.org/10.48550/arxiv.2606.23626","title":"DiT-Reward: Generative Representations for Text-to-Image Reward Modeling","display_name":"DiT-Reward: Generative Representations for Text-to-Image Reward Modeling","publication_year":2026,"publication_date":"2026-06-22","ids":{"openalex":"https://openalex.org/W7165619952","doi":"https://doi.org/10.48550/arxiv.2606.23626"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.23626","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.23626","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.2606.23626","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5139173659","display_name":"Yuanming Yang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Yuanming","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139183975","display_name":"Guoqing Ma","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ma, Guoqing","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139167954","display_name":"Bo Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Bo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139203552","display_name":"Yuan Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Yuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139191940","display_name":"Wei Tang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tang, Wei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139202418","display_name":"Chenyi Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Chenyi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5122964295","display_name":"H Huang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Huang, Haoyang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5139215881","display_name":"Nan Duan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Duan, Nan","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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.6717000007629395,"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":0.6717000007629395,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.20839999616146088,"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/T10028","display_name":"Topic Modeling","score":0.008100000210106373,"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/generative-grammar","display_name":"Generative grammar","score":0.7213000059127808},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.7128999829292297},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.6676999926567078},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.6532999873161316},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.46059998869895935},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.45080000162124634},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.41200000047683716},{"id":"https://openalex.org/keywords/granularity","display_name":"Granularity","score":0.4072999954223633}],"concepts":[{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.7213000059127808},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.7128999829292297},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7044000029563904},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.6676999926567078},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.6532999873161316},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6194999814033508},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.46059998869895935},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.45080000162124634},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.41200000047683716},{"id":"https://openalex.org/C177774035","wikidata":"https://www.wikidata.org/wiki/Q1246948","display_name":"Granularity","level":2,"score":0.4072999954223633},{"id":"https://openalex.org/C99844830","wikidata":"https://www.wikidata.org/wiki/Q102441924","display_name":"Scaling","level":2,"score":0.38909998536109924},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.38659998774528503},{"id":"https://openalex.org/C68339613","wikidata":"https://www.wikidata.org/wiki/Q1549489","display_name":"Speedup","level":2,"score":0.373199999332428},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.3188000023365021},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3068000078201294},{"id":"https://openalex.org/C2781249084","wikidata":"https://www.wikidata.org/wiki/Q908656","display_name":"Preference","level":2,"score":0.30410000681877136},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.2782999873161316},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.26570001244544983},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.2572000026702881},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.25440001487731934}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.23626","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.23626","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.2606.23626","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.23626","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":{"Can":[0],"representations":[1,52,119,182],"learned":[2,89],"for":[3,183],"image":[4,46,51],"generation":[5],"also":[6,124,161],"support":[7],"the":[8,57,82,112,148],"evaluation":[9],"of":[10,22],"generated":[11],"images?":[12],"We":[13,123],"study":[14],"text-to-image":[15,36],"reward":[16,41,107,184],"prediction":[17],"as":[18,62],"a":[19,34,40,87,163],"downstream":[20,106],"task":[21],"generative":[23,83,130,178],"representation":[24],"learning.":[25],"To":[26],"this":[27],"end,":[28],"we":[29],"introduce":[30],"DiT-Reward,":[31],"which":[32],"converts":[33],"pretrained":[35,177],"Diffusion":[37,139],"Transformer":[38],"into":[39],"model":[42],"by":[43],"processing":[44],"near-clean":[45],"latents":[47],"and":[48,77,115,186],"aggregating":[49],"text-conditioned":[50],"across":[53,101,120],"transformer":[54],"layers.":[55],"Under":[56],"same":[58],"training":[59,150],"data":[60],"mixture":[61],"HPSv3,":[63],"DiT-Reward":[64,144],"outperforms":[65,145],"HPSv3":[66,146,168],"on":[67,75,79],"all":[68],"four":[69],"evaluated":[70],"preference":[71,95],"benchmarks,":[72],"reaching":[73],"85.6%":[74],"HPDv2":[76],"77.6%":[78],"HPDv3.":[80],"When":[81],"backbone":[84,131],"is":[85,109],"frozen,":[86],"lightweight":[88],"head":[90],"can":[91],"still":[92],"extract":[93],"meaningful":[94],"predictions":[96],"from":[97,117],"its":[98],"representations.":[99],"Probing":[100],"depth":[102],"further":[103],"reveals":[104],"that":[105,176],"performance":[108],"strongest":[110],"in":[111,156],"middle-to-late":[113],"layers":[114],"benefits":[116],"combining":[118],"different":[121],"stages.":[122],"observe":[125],"consistent":[126],"positive":[127],"scaling":[128],"with":[129,142,152,169],"capacity.":[132],"Finally,":[133],"when":[134],"used":[135],"to":[136],"optimize":[137],"Stable":[138],"3.5":[140],"Large":[141],"Flow-GRPO,":[143],"along":[147],"matched":[149],"trajectory,":[151],"particularly":[153],"clear":[154],"gains":[155],"realism.":[157],"Direct":[158],"latent":[159],"scoring":[160],"achieves":[162],"1.65x":[164],"inference":[165],"speedup":[166],"over":[167],"comparable":[170],"peak":[171],"memory.":[172],"These":[173],"results":[174],"show":[175],"DiTs":[179],"provide":[180],"transferable":[181],"modeling":[185],"policy":[187],"optimization.":[188]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-24T00:00:00"}
