{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:AAYZJP23PTCLE66NTOJ22SYKR6","short_pith_number":"pith:AAYZJP23","schema_version":"1.0","canonical_sha256":"003194bf5b7cc4b27bcd9b93ad4b0a8f988977945f0680b07ae631260c7c40c2","source":{"kind":"arxiv","id":"2505.18877","version":4},"attestation_state":"computed","paper":{"title":"RefLoRA: Refactored Low-Rank Adaptation for Efficient Fine-Tuning of Large Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Bingcong Li, Georgios B. Giannakis, Yilang Zhang","submitted_at":"2025-05-24T21:33:16Z","abstract_excerpt":"Low-Rank Adaptation (LoRA) lowers the computational and memory overhead of fine-tuning large models by updating a low-dimensional subspace of the pre-trained weight matrix. Albeit efficient, LoRA exhibits suboptimal convergence and noticeable performance degradation, due to inconsistent and imbalanced weight updates induced by its nonunique low-rank factorizations. To overcome these limitations, this article identifies the optimal low-rank factorization per step that minimizes an upper bound on the loss. The resultant refactored low-rank adaptation (RefLoRA) method promotes a flatter loss land"},"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":"2505.18877","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-05-24T21:33:16Z","cross_cats_sorted":[],"title_canon_sha256":"8409a9ead32b592306d86c6304840e86185a95ed55c81fc73834873ed58df475","abstract_canon_sha256":"2866608a65263f389c32b4f9c4926cd159a7ef9dcbff9bf8046d394d6fd9f626"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-02T01:03:31.655406Z","signature_b64":"50IIzx/JTs9s8xh0hZSwtnASaBNSHE9W2MTsDQagwAXiZ48/0YNTeNddZn+NVwh/a94jlThhBWGuABqROgS1AQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"003194bf5b7cc4b27bcd9b93ad4b0a8f988977945f0680b07ae631260c7c40c2","last_reissued_at":"2026-06-02T01:03:31.654849Z","signature_status":"signed_v1","first_computed_at":"2026-06-02T01:03:31.654849Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"RefLoRA: Refactored Low-Rank Adaptation for Efficient Fine-Tuning of Large Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Bingcong Li, Georgios B. Giannakis, Yilang Zhang","submitted_at":"2025-05-24T21:33:16Z","abstract_excerpt":"Low-Rank Adaptation (LoRA) lowers the computational and memory overhead of fine-tuning large models by updating a low-dimensional subspace of the pre-trained weight matrix. Albeit efficient, LoRA exhibits suboptimal convergence and noticeable performance degradation, due to inconsistent and imbalanced weight updates induced by its nonunique low-rank factorizations. To overcome these limitations, this article identifies the optimal low-rank factorization per step that minimizes an upper bound on the loss. The resultant refactored low-rank adaptation (RefLoRA) method promotes a flatter loss land"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.18877","kind":"arxiv","version":4},"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/2505.18877/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":"2505.18877","created_at":"2026-06-02T01:03:31.654909+00:00"},{"alias_kind":"arxiv_version","alias_value":"2505.18877v4","created_at":"2026-06-02T01:03:31.654909+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.18877","created_at":"2026-06-02T01:03:31.654909+00:00"},{"alias_kind":"pith_short_12","alias_value":"AAYZJP23PTCL","created_at":"2026-06-02T01:03:31.654909+00:00"},{"alias_kind":"pith_short_16","alias_value":"AAYZJP23PTCLE66N","created_at":"2026-06-02T01:03:31.654909+00:00"},{"alias_kind":"pith_short_8","alias_value":"AAYZJP23","created_at":"2026-06-02T01:03:31.654909+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/AAYZJP23PTCLE66NTOJ22SYKR6","json":"https://pith.science/pith/AAYZJP23PTCLE66NTOJ22SYKR6.json","graph_json":"https://pith.science/api/pith-number/AAYZJP23PTCLE66NTOJ22SYKR6/graph.json","events_json":"https://pith.science/api/pith-number/AAYZJP23PTCLE66NTOJ22SYKR6/events.json","paper":"https://pith.science/paper/AAYZJP23"},"agent_actions":{"view_html":"https://pith.science/pith/AAYZJP23PTCLE66NTOJ22SYKR6","download_json":"https://pith.science/pith/AAYZJP23PTCLE66NTOJ22SYKR6.json","view_paper":"https://pith.science/paper/AAYZJP23","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2505.18877&json=true","fetch_graph":"https://pith.science/api/pith-number/AAYZJP23PTCLE66NTOJ22SYKR6/graph.json","fetch_events":"https://pith.science/api/pith-number/AAYZJP23PTCLE66NTOJ22SYKR6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/AAYZJP23PTCLE66NTOJ22SYKR6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/AAYZJP23PTCLE66NTOJ22SYKR6/action/storage_attestation","attest_author":"https://pith.science/pith/AAYZJP23PTCLE66NTOJ22SYKR6/action/author_attestation","sign_citation":"https://pith.science/pith/AAYZJP23PTCLE66NTOJ22SYKR6/action/citation_signature","submit_replication":"https://pith.science/pith/AAYZJP23PTCLE66NTOJ22SYKR6/action/replication_record"}},"created_at":"2026-06-02T01:03:31.654909+00:00","updated_at":"2026-06-02T01:03:31.654909+00:00"}