{"id":"https://openalex.org/W7159618056","doi":"https://doi.org/10.48550/arxiv.2604.27295","title":"Learning Rate Engineering: From Coarse Single Parameter to Layered Evolution","display_name":"Learning Rate Engineering: From Coarse Single Parameter to Layered Evolution","publication_year":2026,"publication_date":"2026-04-30","ids":{"openalex":"https://openalex.org/W7159618056","doi":"https://doi.org/10.48550/arxiv.2604.27295"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.27295","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.27295","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":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.2604.27295","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5134971599","display_name":"Ming-Hong Yao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yao, Ming-Hong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134946710","display_name":"Di Wang (329735)","orcid":"https://orcid.org/0009-0004-2632-0165"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Di","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134944356","display_name":"Jian Cui","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cui, Jian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134964208","display_name":"Jin-Yan Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Jin-Yan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073820400","display_name":"Zi-hao Cui","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cui, Zi-Hao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134929656","display_name":"Fa Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Fa","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134963683","display_name":"Chen Wei","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wei, Chen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5134997250","display_name":"Qiu-Ye Yu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yu, Qiu-Ye","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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.6362000107765198,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.6362000107765198,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.10819999873638153,"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/T11612","display_name":"Stochastic Gradient Optimization Techniques","score":0.042500000447034836,"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/scaling","display_name":"Scaling","score":0.6733999848365784},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.6028000116348267},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5117999911308289},{"id":"https://openalex.org/keywords/scheduling","display_name":"Scheduling (production processes)","score":0.5088000297546387},{"id":"https://openalex.org/keywords/layer","display_name":"Layer (electronics)","score":0.4259999990463257},{"id":"https://openalex.org/keywords/scratch","display_name":"Scratch","score":0.3901999890804291},{"id":"https://openalex.org/keywords/joint","display_name":"Joint (building)","score":0.3425999879837036}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6757000088691711},{"id":"https://openalex.org/C99844830","wikidata":"https://www.wikidata.org/wiki/Q102441924","display_name":"Scaling","level":2,"score":0.6733999848365784},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.6028000116348267},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5117999911308289},{"id":"https://openalex.org/C206729178","wikidata":"https://www.wikidata.org/wiki/Q2271896","display_name":"Scheduling (production processes)","level":2,"score":0.5088000297546387},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.460099995136261},{"id":"https://openalex.org/C2779227376","wikidata":"https://www.wikidata.org/wiki/Q6505497","display_name":"Layer (electronics)","level":2,"score":0.4259999990463257},{"id":"https://openalex.org/C2781235140","wikidata":"https://www.wikidata.org/wiki/Q275131","display_name":"Scratch","level":2,"score":0.3901999890804291},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.36000001430511475},{"id":"https://openalex.org/C18555067","wikidata":"https://www.wikidata.org/wiki/Q8375051","display_name":"Joint (building)","level":2,"score":0.3425999879837036},{"id":"https://openalex.org/C178009071","wikidata":"https://www.wikidata.org/wiki/Q93344","display_name":"Trigonometric functions","level":2,"score":0.33570000529289246},{"id":"https://openalex.org/C147764199","wikidata":"https://www.wikidata.org/wiki/Q6865248","display_name":"Minification","level":2,"score":0.29829999804496765},{"id":"https://openalex.org/C75291252","wikidata":"https://www.wikidata.org/wiki/Q1315756","display_name":"TRACE (psycholinguistics)","level":2,"score":0.29330000281333923},{"id":"https://openalex.org/C136886441","wikidata":"https://www.wikidata.org/wiki/Q926129","display_name":"Normalization (sociology)","level":2,"score":0.2870999872684479},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.2856000065803528},{"id":"https://openalex.org/C2988430800","wikidata":"https://www.wikidata.org/wiki/Q428971","display_name":"Scaling law","level":3,"score":0.2808000147342682},{"id":"https://openalex.org/C164752517","wikidata":"https://www.wikidata.org/wiki/Q5570875","display_name":"Global optimization","level":2,"score":0.2540999948978424},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.25189998745918274}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.27295","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.27295","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":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.2604.27295","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.27295","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.6887593269348145}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Learning":[0],"rate":[1,10],"scheduling":[2],"has":[3],"evolved":[4],"from":[5,57,193],"the":[6,47,55,66,152,182,214],"single":[7,121,174],"global":[8,27,32],"fixed":[9,28],"of":[11,69],"early":[12],"SGD":[13],"to":[14,59,77,87,89],"sophisticated":[15],"layer-wise":[16],"adaptive":[17],"strategies.":[18],"We":[19,45,124],"systematize":[20],"this":[21,94],"evolution":[22],"into":[23,119],"five":[24,134,137],"generations:":[25],"(Gen1)":[26],"learning":[29],"rates,":[30],"(Gen2)":[31],"scheduling,":[33,110],"(Gen3)":[34],"parameter-level":[35],"adaptation,":[36],"(Gen4)":[37],"layer-level":[38],"differentiation,":[39],"and":[40,63,115,145],"(Gen5)":[41],"joint":[42],"layer-time":[43],"scheduling.":[44],"trace":[46],"fundamental":[48],"motivation":[49],"behind":[50],"each":[51],"transition,":[52],"showing":[53],"how":[54],"shift":[56],"one-size-fits-all":[58],"tailoring":[60],"by":[61],"layer":[62],"time":[64],"addresses":[65],"impossible":[67],"trinity":[68],"transfer":[70],"learning:":[71],"lower":[72],"layers":[73,83],"require":[74],"small":[75],"updates":[76,86],"preserve":[78],"general":[79],"knowledge":[80],"while":[81,157,218],"higher":[82],"need":[84],"large":[85],"adapt":[88],"new":[90],"tasks.":[91],"Building":[92],"on":[93,136,187,191],"taxonomy,":[95],"we":[96],"propose":[97],"Discriminative":[98],"Adaptive":[99],"Layer":[100],"Scaling":[101],"(DALS),":[102],"a":[103,120],"unified":[104],"framework":[105],"that":[106,200],"integrates":[107],"phase-adaptive":[108],"cosine":[109],"depth-aware":[111],"Grokfast":[112],"gradient":[113],"filtering,":[114],"LARS-style":[116],"trust":[117],"ratios":[118],"coherent":[122],"optimizer.":[123],"benchmark":[125],"18":[126],"strategies":[127],"including":[128],"three":[129],"DALS":[130,150,209],"variants":[131],"across":[132,177],"all":[133,178],"generations":[135],"datasets:":[138],"synthetic,":[139,149],"CIFAR-10":[140],"(from":[141],"scratch),":[142],"RTE,":[143],"TREC-6,":[144],"IMDb":[146],"(fine-tuning).":[147],"On":[148],"achieves":[151],"best":[153,215],"accuracy":[154],"at":[155],"98.0%,":[156],"DALS-Fast":[158],"reaches":[159],"90%":[160],"in":[161],"just":[162],"3":[163],"epochs.":[164],"The":[165],"cross-dataset":[166],"analysis":[167],"reveals":[168],"striking":[169],"regime-dependent":[170],"patterns":[171],"--":[172],"no":[173],"strategy":[175],"wins":[176],"regimes.":[179],"Critically,":[180],"STLR+Discriminative,":[181],"ULMFiT":[183],"champion,":[184],"catastrophically":[185],"fails":[186],"from-scratch":[188],"tasks":[189],"(43.6%":[190],"TREC-6":[192],"scratch":[194],"vs.":[195],"96.8%":[196],"with":[197],"RAdam),":[198],"confirming":[199],"directional":[201],"decay":[202],"biases":[203],"are":[204],"harmful":[205],"without":[206],"pretrained":[207],"features.":[208],"avoids":[210],"either":[211],"extreme,":[212],"achieving":[213],"synthetic":[216],"result":[217],"maintaining":[219],"competitive":[220],"fine-tuning":[221],"performance.":[222]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-05-02T00:00:00"}