{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:BADZKZSVOQJCTADX6BHAELMJCK","short_pith_number":"pith:BADZKZSV","schema_version":"1.0","canonical_sha256":"08079566557412298077f04e022d891287a2df78d6cf2521b9a4a9d95f654a4a","source":{"kind":"arxiv","id":"2502.01378","version":1},"attestation_state":"computed","paper":{"title":"CE-LoRA: Computation-Efficient LoRA Fine-Tuning for Language Models","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Binhang Yuan, Guanduo Chen, Kun Yuan, Yipeng Hu, Yutong He","submitted_at":"2025-02-03T14:15:33Z","abstract_excerpt":"Large Language Models (LLMs) demonstrate exceptional performance across various tasks but demand substantial computational resources even for fine-tuning computation. Although Low-Rank Adaptation (LoRA) significantly alleviates memory consumption during fine-tuning, its impact on computational cost reduction is limited. This paper identifies the computation of activation gradients as the primary bottleneck in LoRA's backward propagation and introduces the Computation-Efficient LoRA (CE-LoRA) algorithm, which enhances computational efficiency while preserving memory efficiency. CE-LoRA leverage"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2502.01378","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2025-02-03T14:15:33Z","cross_cats_sorted":[],"title_canon_sha256":"8f451e69489db6aa22ed9d7cc46c68fcd690cd434493b03b65e7832ca2176db3","abstract_canon_sha256":"1e504c35466fbeaefdcd3a4c411f361158daca67a5b7f5e9ff42cf2a80ae8091"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:08:46.492909Z","signature_b64":"5PFim987ICjuDcWl5aba0vYE5DAdoPC+zIgOl/t57Wcs0l9Xe7ladPIYUUzVdwWN46+Yn5+GamYzEmXbEDjAAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"08079566557412298077f04e022d891287a2df78d6cf2521b9a4a9d95f654a4a","last_reissued_at":"2026-07-05T10:08:46.492437Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:08:46.492437Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"CE-LoRA: Computation-Efficient LoRA Fine-Tuning for Language Models","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Binhang Yuan, Guanduo Chen, Kun Yuan, Yipeng Hu, Yutong He","submitted_at":"2025-02-03T14:15:33Z","abstract_excerpt":"Large Language Models (LLMs) demonstrate exceptional performance across various tasks but demand substantial computational resources even for fine-tuning computation. Although Low-Rank Adaptation (LoRA) significantly alleviates memory consumption during fine-tuning, its impact on computational cost reduction is limited. This paper identifies the computation of activation gradients as the primary bottleneck in LoRA's backward propagation and introduces the Computation-Efficient LoRA (CE-LoRA) algorithm, which enhances computational efficiency while preserving memory efficiency. CE-LoRA leverage"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.01378","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2502.01378/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2502.01378","created_at":"2026-07-05T10:08:46.492495+00:00"},{"alias_kind":"arxiv_version","alias_value":"2502.01378v1","created_at":"2026-07-05T10:08:46.492495+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.01378","created_at":"2026-07-05T10:08:46.492495+00:00"},{"alias_kind":"pith_short_12","alias_value":"BADZKZSVOQJC","created_at":"2026-07-05T10:08:46.492495+00:00"},{"alias_kind":"pith_short_16","alias_value":"BADZKZSVOQJCTADX","created_at":"2026-07-05T10:08:46.492495+00:00"},{"alias_kind":"pith_short_8","alias_value":"BADZKZSV","created_at":"2026-07-05T10:08:46.492495+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.11141","citing_title":"From over-reliance to smart integration: using Large-Language Models as translators between specialized modeling and simulation tools","ref_index":2025,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/BADZKZSVOQJCTADX6BHAELMJCK","json":"https://pith.science/pith/BADZKZSVOQJCTADX6BHAELMJCK.json","graph_json":"https://pith.science/api/pith-number/BADZKZSVOQJCTADX6BHAELMJCK/graph.json","events_json":"https://pith.science/api/pith-number/BADZKZSVOQJCTADX6BHAELMJCK/events.json","paper":"https://pith.science/paper/BADZKZSV"},"agent_actions":{"view_html":"https://pith.science/pith/BADZKZSVOQJCTADX6BHAELMJCK","download_json":"https://pith.science/pith/BADZKZSVOQJCTADX6BHAELMJCK.json","view_paper":"https://pith.science/paper/BADZKZSV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2502.01378&json=true","fetch_graph":"https://pith.science/api/pith-number/BADZKZSVOQJCTADX6BHAELMJCK/graph.json","fetch_events":"https://pith.science/api/pith-number/BADZKZSVOQJCTADX6BHAELMJCK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BADZKZSVOQJCTADX6BHAELMJCK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BADZKZSVOQJCTADX6BHAELMJCK/action/storage_attestation","attest_author":"https://pith.science/pith/BADZKZSVOQJCTADX6BHAELMJCK/action/author_attestation","sign_citation":"https://pith.science/pith/BADZKZSVOQJCTADX6BHAELMJCK/action/citation_signature","submit_replication":"https://pith.science/pith/BADZKZSVOQJCTADX6BHAELMJCK/action/replication_record"}},"created_at":"2026-07-05T10:08:46.492495+00:00","updated_at":"2026-07-05T10:08:46.492495+00:00"}