{"id":"https://openalex.org/W7162670702","doi":"https://doi.org/10.48550/arxiv.2605.28573","title":"Efficient Pre-Training of LLMs through Truncated SVD Layers","display_name":"Efficient Pre-Training of LLMs through Truncated SVD Layers","publication_year":2026,"publication_date":"2026-05-27","ids":{"openalex":"https://openalex.org/W7162670702","doi":"https://doi.org/10.48550/arxiv.2605.28573"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.28573","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.28573","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.2605.28573","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5086460627","display_name":"Kaivan Kamali","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kamali, Kaivan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5008919064","display_name":"Kajetan Schweighofer","orcid":"https://orcid.org/0009-0001-0482-2699"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Schweighofer, Kajetan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5034397027","display_name":"Hormoz Shahrzad","orcid":"https://orcid.org/0000-0002-5983-4531"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shahrzad, Hormoz","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5002978952","display_name":"Olivier Francon","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Francon, Olivier","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035553462","display_name":"Babak Hodjat","orcid":"https://orcid.org/0000-0002-4547-4731"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hodjat, Babak","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5137230410","display_name":"Risto Miikkulainen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Miikkulainen, Risto","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/T11612","display_name":"Stochastic Gradient Optimization Techniques","score":0.18860000371932983,"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/T11612","display_name":"Stochastic Gradient Optimization Techniques","score":0.18860000371932983,"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/T10028","display_name":"Topic Modeling","score":0.09019999951124191,"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":0.08160000294446945,"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/orthonormality","display_name":"Orthonormality","score":0.8758000135421753},{"id":"https://openalex.org/keywords/rank","display_name":"Rank (graph theory)","score":0.7170000076293945},{"id":"https://openalex.org/keywords/orthonormal-basis","display_name":"Orthonormal basis","score":0.6464999914169312},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.5866000056266785},{"id":"https://openalex.org/keywords/path","display_name":"Path (computing)","score":0.5425999760627747},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5393999814987183},{"id":"https://openalex.org/keywords/heuristic","display_name":"Heuristic","score":0.48840001225471497},{"id":"https://openalex.org/keywords/scaling","display_name":"Scaling","score":0.48089998960494995}],"concepts":[{"id":"https://openalex.org/C92951342","wikidata":"https://www.wikidata.org/wiki/Q1411166","display_name":"Orthonormality","level":3,"score":0.8758000135421753},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.7170000076293945},{"id":"https://openalex.org/C5806529","wikidata":"https://www.wikidata.org/wiki/Q2365325","display_name":"Orthonormal basis","level":2,"score":0.6464999914169312},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.5866000056266785},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.574400007724762},{"id":"https://openalex.org/C2777735758","wikidata":"https://www.wikidata.org/wiki/Q817765","display_name":"Path (computing)","level":2,"score":0.5425999760627747},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5393999814987183},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.510699987411499},{"id":"https://openalex.org/C173801870","wikidata":"https://www.wikidata.org/wiki/Q201413","display_name":"Heuristic","level":2,"score":0.48840001225471497},{"id":"https://openalex.org/C99844830","wikidata":"https://www.wikidata.org/wiki/Q102441924","display_name":"Scaling","level":2,"score":0.48089998960494995},{"id":"https://openalex.org/C22789450","wikidata":"https://www.wikidata.org/wiki/Q420904","display_name":"Singular value decomposition","level":2,"score":0.4722000062465668},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.4512999951839447},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.4350000023841858},{"id":"https://openalex.org/C90199385","wikidata":"https://www.wikidata.org/wiki/Q6692777","display_name":"Low-rank approximation","level":3,"score":0.36550000309944153},{"id":"https://openalex.org/C179799912","wikidata":"https://www.wikidata.org/wiki/Q205084","display_name":"Computational complexity theory","level":2,"score":0.3560999929904938},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.35519999265670776},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.33709999918937683},{"id":"https://openalex.org/C88626702","wikidata":"https://www.wikidata.org/wiki/Q1128903","display_name":"Continuation","level":2,"score":0.3312000036239624},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.32710000872612},{"id":"https://openalex.org/C124681953","wikidata":"https://www.wikidata.org/wiki/Q339062","display_name":"Decomposition","level":2,"score":0.3116999864578247},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.30790001153945923},{"id":"https://openalex.org/C134466208","wikidata":"https://www.wikidata.org/wiki/Q1520657","display_name":"Weight function","level":2,"score":0.2994999885559082},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.2870999872684479},{"id":"https://openalex.org/C149364088","wikidata":"https://www.wikidata.org/wiki/Q185917","display_name":"Translation (biology)","level":4,"score":0.2614000141620636}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.28573","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.28573","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.2605.28573","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.28573","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":{"The":[0,119],"massive":[1],"scaling":[2],"of":[3,87,111],"Large":[4],"Language":[5],"Models":[6],"(LLMs)":[7],"has":[8],"made":[9],"pretraining":[10,91],"increasingly":[11],"cost-prohibitive.":[12],"While":[13],"low-rank":[14],"representation":[15],"and":[16,26,37,58,75,93,126],"orthonormal":[17],"weight":[18,41],"matrices":[19],"could":[20],"in":[21,90],"principle":[22],"reduce":[23],"parameter":[24],"counts":[25],"computational":[27,46],"overhead,":[28],"most":[29],"existing":[30],"methods":[31],"rely":[32],"on":[33],"static":[34],"rank":[35,57,73],"selection":[36],"do":[38],"not":[39],"enforce":[40],"orthonormality":[42,60],"due":[43],"to":[44,79],"high":[45],"cost.":[47],"This":[48],"paper":[49],"introduces":[50],"TSVD,":[51],"a":[52,67,76,123],"framework":[53],"that":[54,100],"maintains":[55],"low":[56],"strict":[59],"throughout":[61],"the":[62,85,88,109],"training":[63],"process.":[64],"It":[65],"utilizes":[66],"spectral":[68],"energy-based":[69],"heuristic":[70],"for":[71],"adaptive":[72],"selection,":[74],"caching":[77],"mechanisms":[78],"maintain":[80],"orthonormality.":[81],"Theoretical":[82],"analysis":[83],"justifies":[84],"advantage":[86],"approach":[89,120],"dynamics":[92],"experiments":[94],"across":[95],"various":[96],"model":[97],"scales":[98],"demonstrate":[99],"it":[101],"is":[102],"effective":[103],"empirically.":[104],"TSVD":[105],"matches":[106],"or":[107],"exceeds":[108],"performance":[110],"full-parameter":[112],"baselines":[113],"while":[114],"significantly":[115],"reducing":[116],"compute":[117],"requirements.":[118],"thus":[121],"offers":[122],"well-founded,":[124],"practical,":[125],"scalable":[127],"path":[128],"toward":[129],"efficient":[130],"high-performance":[131],"LLM":[132],"pretraining.":[133]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-29T00:00:00"}