{"id":"https://openalex.org/W7169883300","doi":"https://doi.org/10.48550/arxiv.2607.17572","title":"JAGG: Jacobian-Aggregated Group Gradient for Efficient GRPO Training of Diffusion Models","display_name":"JAGG: Jacobian-Aggregated Group Gradient for Efficient GRPO Training of Diffusion Models","publication_year":2026,"publication_date":"2026-07-20","ids":{"openalex":"https://openalex.org/W7169883300","doi":"https://doi.org/10.48550/arxiv.2607.17572"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2607.17572","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.17572","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":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.2607.17572","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101213772","display_name":"Ruiyi Ding","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ding, Ruiyi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5141297314","display_name":"Jie Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Jie","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134385769","display_name":"He Kang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kang, He","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5141270423","display_name":"Ziyan Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Ziyan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5141248946","display_name":"Chengru Song","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Song, Chengru","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5141275360","display_name":"Yuan chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"cheng, Yuan","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":0.7163000106811523,"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":0.7163000106811523,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.05290000140666962,"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":0.029400000348687172,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/speedup","display_name":"Speedup","score":0.6331999897956848},{"id":"https://openalex.org/keywords/interpolation","display_name":"Interpolation (computer graphics)","score":0.5791000127792358},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5012000203132629},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.47999998927116394},{"id":"https://openalex.org/keywords/acceleration","display_name":"Acceleration","score":0.45509999990463257},{"id":"https://openalex.org/keywords/structured-prediction","display_name":"Structured prediction","score":0.37599998712539673},{"id":"https://openalex.org/keywords/linear-interpolation","display_name":"Linear interpolation","score":0.35749998688697815},{"id":"https://openalex.org/keywords/traverse","display_name":"Traverse","score":0.3481999933719635},{"id":"https://openalex.org/keywords/early-stopping","display_name":"Early stopping","score":0.3377000093460083}],"concepts":[{"id":"https://openalex.org/C68339613","wikidata":"https://www.wikidata.org/wiki/Q1549489","display_name":"Speedup","level":2,"score":0.6331999897956848},{"id":"https://openalex.org/C137800194","wikidata":"https://www.wikidata.org/wiki/Q11713455","display_name":"Interpolation (computer graphics)","level":3,"score":0.5791000127792358},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5740000009536743},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5012000203132629},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5006999969482422},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.47999998927116394},{"id":"https://openalex.org/C117896860","wikidata":"https://www.wikidata.org/wiki/Q11376","display_name":"Acceleration","level":2,"score":0.45509999990463257},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4350999891757965},{"id":"https://openalex.org/C22367795","wikidata":"https://www.wikidata.org/wiki/Q7625208","display_name":"Structured prediction","level":2,"score":0.37599998712539673},{"id":"https://openalex.org/C171836373","wikidata":"https://www.wikidata.org/wiki/Q2266329","display_name":"Linear interpolation","level":3,"score":0.35749998688697815},{"id":"https://openalex.org/C176809094","wikidata":"https://www.wikidata.org/wiki/Q15401496","display_name":"Traverse","level":2,"score":0.3481999933719635},{"id":"https://openalex.org/C5465570","wikidata":"https://www.wikidata.org/wiki/Q5326898","display_name":"Early stopping","level":3,"score":0.3377000093460083},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.3257000148296356},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.32120001316070557},{"id":"https://openalex.org/C77170095","wikidata":"https://www.wikidata.org/wiki/Q1753188","display_name":"Linearity","level":2,"score":0.30309998989105225},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.3019999861717224},{"id":"https://openalex.org/C69357855","wikidata":"https://www.wikidata.org/wiki/Q163214","display_name":"Diffusion","level":2,"score":0.3010999858379364},{"id":"https://openalex.org/C38349280","wikidata":"https://www.wikidata.org/wiki/Q1434290","display_name":"Flow (mathematics)","level":2,"score":0.2935999929904938},{"id":"https://openalex.org/C160403385","wikidata":"https://www.wikidata.org/wiki/Q220543","display_name":"Queue","level":2,"score":0.2903999984264374},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.275299996137619},{"id":"https://openalex.org/C92757383","wikidata":"https://www.wikidata.org/wiki/Q382497","display_name":"Affine transformation","level":2,"score":0.2750999927520752},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.273499995470047},{"id":"https://openalex.org/C64208722","wikidata":"https://www.wikidata.org/wiki/Q861606","display_name":"Lagrange polynomial","level":3,"score":0.26930001378059387},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.266400009393692},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.2662000060081482},{"id":"https://openalex.org/C2781311116","wikidata":"https://www.wikidata.org/wiki/Q83306","display_name":"Group (periodic table)","level":2,"score":0.2578999996185303},{"id":"https://openalex.org/C147297375","wikidata":"https://www.wikidata.org/wiki/Q6674930","display_name":"Look-ahead","level":2,"score":0.25369998812675476},{"id":"https://openalex.org/C138777275","wikidata":"https://www.wikidata.org/wiki/Q6884054","display_name":"Mixing (physics)","level":2,"score":0.25279998779296875}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2607.17572","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.17572","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"doi:10.48550/arxiv.2607.17572","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.17572","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"score":0.45199334621429443,"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Group":[0],"Relative":[1],"Policy":[2],"Optimization":[3],"(GRPO)":[4],"is":[5,156,161],"a":[6,33,147,166],"powerful":[7],"reinforcement":[8],"learning":[9],"algorithm":[10],"for":[11,194],"aligning":[12],"generative":[13],"models":[14,31],"with":[15,104,188],"human":[16],"preferences.":[17],"While":[18],"successful":[19],"in":[20,163],"large":[21],"language":[22],"models~\\cite{shao2024deepseekmathpushinglimitsmathematical},":[23],"its":[24],"extension":[25],"to":[26,117],"diffusion":[27],"and":[28,75,80,101,165],"flow":[29],"matching":[30],"introduces":[32],"severe":[34],"computational":[35],"bottleneck:":[36],"gradients":[37,144],"must":[38],"be":[39,198],"back-propagated":[40],"through":[41,146,200],"the":[42,50,69,84,89,94,133,159,175],"high-capacity":[43],"DiT":[44,61,72,98],"backbone":[45],"at":[46],"\\emph{every}":[47],"timestep":[48],"of":[49,97,121,132],"sampling":[51],"trajectory,":[52],"making":[53],"high-resolution":[54],"text-to-image":[55],"(T2I)":[56],"training":[57],"prohibitively":[58],"expensive.":[59],"Training-free":[60],"inference":[62],"acceleration":[63],"methods":[64],"(e.g.,":[65],"$\u0394$-DiT,":[66],"ScalingCache)":[67],"exploit":[68],"fact":[70],"that":[71],"hidden":[73],"states":[74],"velocity":[76,160],"predictions":[77],"vary":[78],"\\emph{smoothly":[79],"nearly":[81],"linearly}":[82],"along":[83],"trajectory.":[85],"We":[86,152],"ask":[87],"whether":[88],"same":[90],"linearity":[91],"can":[92,197],"reduce":[93],"backward-pass":[95],"cost":[96],"RL":[99],"training,":[100],"answer":[102],"affirmatively":[103],"\\textbf{JAGG}":[105],"(\\textbf{J}acobian-\\textbf{A}ggregated":[106],"\\textbf{G}roup":[107],"\\textbf{G}radient),":[108],"which":[109],"reduces":[110],"full":[111],"transformer":[112],"backward":[113,150,186],"passes":[114],"from":[115],"$W$":[116,122],"$2$":[118],"per":[119],"group":[120],"consecutive":[123],"steps.":[124],"JAGG":[125,172,183],"approximates":[126],"intermediate-step":[127],"Jacobians":[128],"via":[129],"$t$-weighted":[130],"interpolation":[131,155],"endpoint":[134],"Jacobians,":[135],"then":[136],"aggregates":[137],"per-step":[138],"upstream":[139],"signals":[140],"into":[141],"two":[142],"composite":[143],"applied":[145],"single":[148],"joint":[149],"pass.":[151],"prove":[153],"this":[154,195],"\\emph{exact}":[157],"when":[158],"linear":[162],"$(z,t)$,":[164],"cosine-similarity":[167],"routing":[168],"rule":[169],"(\\texttt{jagg\\_frac})":[170],"deploys":[171],"only":[173],"where":[174],"assumption":[176],"holds.":[177],"Experiments":[178],"on":[179],"T2I":[180],"benchmarks":[181],"show":[182],"delivers":[184],"$\\sim$2$\\times$":[185],"speedup":[187],"negligible":[189],"quality":[190],"degradation.":[191],"The":[192],"code":[193],"work":[196],"accessed":[199],"https://github.com/SchumiDing/JAGG.":[201]},"counts_by_year":[],"updated_date":"2026-07-29T09:40:50.615796","created_date":"2026-07-22T00:00:00"}