{"id":"https://openalex.org/W7165666045","doi":"https://doi.org/10.48550/arxiv.2606.22189","title":"L20-Edu-135M: An Auditable Single-GPU Study of Data-Efficient Small Language Modeling","display_name":"L20-Edu-135M: An Auditable Single-GPU Study of Data-Efficient Small Language Modeling","publication_year":2026,"publication_date":"2026-06-20","ids":{"openalex":"https://openalex.org/W7165666045","doi":"https://doi.org/10.48550/arxiv.2606.22189"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.22189","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.22189","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":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.2606.22189","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5139167749","display_name":"Yin Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Li, Yin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":["https://openalex.org/A5139167749"],"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/T10181","display_name":"Natural Language Processing Techniques","score":0.20679999887943268,"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/T10181","display_name":"Natural Language Processing Techniques","score":0.20679999887943268,"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/T10054","display_name":"Parallel Computing and Optimization Techniques","score":0.18299999833106995,"subfield":{"id":"https://openalex.org/subfields/1708","display_name":"Hardware and Architecture"},"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/T14347","display_name":"Big Data and Digital Economy","score":0.1151999980211258,"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/security-token","display_name":"Security token","score":0.7491999864578247},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.6334999799728394},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.5814999938011169},{"id":"https://openalex.org/keywords/equivalence","display_name":"Equivalence (formal languages)","score":0.5174000263214111},{"id":"https://openalex.org/keywords/verifiable-secret-sharing","display_name":"Verifiable secret sharing","score":0.42969998717308044},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.36090001463890076},{"id":"https://openalex.org/keywords/statistical-model","display_name":"Statistical model","score":0.35269999504089355}],"concepts":[{"id":"https://openalex.org/C48145219","wikidata":"https://www.wikidata.org/wiki/Q1335365","display_name":"Security token","level":2,"score":0.7491999864578247},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7329999804496765},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.6334999799728394},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.5814999938011169},{"id":"https://openalex.org/C2780069185","wikidata":"https://www.wikidata.org/wiki/Q7977945","display_name":"Equivalence (formal languages)","level":2,"score":0.5174000263214111},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4496999979019165},{"id":"https://openalex.org/C85847156","wikidata":"https://www.wikidata.org/wiki/Q59015987","display_name":"Verifiable secret sharing","level":3,"score":0.42969998717308044},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.38690000772476196},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.36090001463890076},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.35269999504089355},{"id":"https://openalex.org/C48677424","wikidata":"https://www.wikidata.org/wiki/Q6888088","display_name":"Mode (computer interface)","level":2,"score":0.3240000009536743},{"id":"https://openalex.org/C2779280203","wikidata":"https://www.wikidata.org/wiki/Q17121211","display_name":"Small data","level":2,"score":0.3077000081539154},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.28999999165534973},{"id":"https://openalex.org/C99844830","wikidata":"https://www.wikidata.org/wiki/Q102441924","display_name":"Scaling","level":2,"score":0.2757999897003174},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.272599995136261},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.27149999141693115},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.2590000033378601},{"id":"https://openalex.org/C77553402","wikidata":"https://www.wikidata.org/wiki/Q13222579","display_name":"Upper and lower bounds","level":2,"score":0.2547000050544739},{"id":"https://openalex.org/C94375191","wikidata":"https://www.wikidata.org/wiki/Q11205","display_name":"Arithmetic","level":1,"score":0.25110000371932983}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.22189","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.22189","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":"doi:10.48550/arxiv.2606.22189","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.22189","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":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"display_name":"Quality Education","score":0.4524655342102051,"id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Small":[0],"language":[1],"models":[2],"are":[3,17],"cheap":[4],"to":[5,24,171],"serve":[6],"and":[7,67,91,117,175],"feasible":[8],"on":[9,28,43,98,194],"local":[10],"hardware,":[11],"but":[12,120],"strong":[13],"public":[14,156],"135M-class":[15],"systems":[16],"commonly":[18],"trained":[19],"with":[20,88],"hundreds":[21],"of":[22,26,111,125,142],"billions":[23],"trillions":[25],"tokens":[27,59],"large":[29],"clusters.":[30],"We":[31,70],"study":[32],"a":[33,37,62,101,108,146,183,190,205],"sharply":[34],"resource-constrained":[35,201],"regime:":[36],"complete":[38],"134.5M-parameter":[39],"language-model":[40],"pipeline":[41],"executed":[42],"one":[44],"NVIDIA":[45],"L20":[46],"GPU.":[47],"The":[48,150,196],"released":[49],"checkpoint,":[50],"L20-Edu-135M,":[51],"receives":[52],"approximately":[53],"13B":[54],"pretraining":[55],"tokens:":[56],"10B":[57],"FineWeb-Edu":[58],"followed":[60],"by":[61],"3B-token":[63],"educational,":[64],"mathematics,":[65],"code,":[66],"reasoning":[68],"mixture.":[69],"document":[71],"the":[72,159],"architecture,":[73],"data":[74],"gates,":[75],"cross-source":[76],"MinHash/LSH":[77],"near-deduplication,":[78],"segment":[79],"deduplication,":[80],"benchmark-overlap":[81],"removal,":[82],"throughput":[83],"optimization,":[84],"supervised":[85],"fine-tuning":[86],"(SFT)":[87],"weight":[89],"interpolation,":[90],"reinforcement":[92],"learning":[93],"from":[94,169],"verifiable":[95],"rewards":[96],"(RLVR)":[97],"GSM8K.":[99],"In":[100],"self-run":[102],"zero-shot":[103],"six-task":[104],"harness,":[105],"L20-Edu-135M":[106],"obtains":[107],"mean":[109,122],"score":[110],"0.4150.":[112],"It":[113],"trails":[114],"SmolLM-135M":[115],"(0.4767)":[116],"SmolLM2-135M":[118],"(0.4917),":[119],"its":[121,128],"is":[123,132,138,198],"87.1%":[124],"SmolLM-135M's":[126],"while":[127],"nominal":[129],"token":[130],"count":[131],"2.17%":[133],"as":[134],"large.":[135],"This":[136],"ratio":[137],"descriptive,":[139],"not":[140,204],"evidence":[141],"statistical":[143],"equivalence":[144],"or":[145],"controlled":[147],"scaling":[148],"law.":[149],"model":[151],"exceeds":[152],"several":[153],"older":[154],"100M-160M":[155],"baselines":[157],"under":[158],"same":[160],"harness.":[161],"Direct":[162],"GRPO-style":[163],"RLVR":[164],"decreases":[165],"GSM8K":[166],"exact-match":[167],"accuracy":[168],"1.82%":[170],"1.59%":[172],"(192-token":[173],"completions)":[174],"1.21%":[176],"(320-token":[177],"completions).":[178],"These":[179],"single-run":[180],"results":[181],"identify":[182],"concrete":[184],"failure":[185],"mode":[186],"rather":[187],"than":[188],"establishing":[189],"general":[191],"lower":[192],"bound":[193],"RLVR.":[195],"contribution":[197],"an":[199],"auditable":[200],"case":[202],"study,":[203],"state-of-the-art":[206],"claim.":[207]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-24T00:00:00"}