{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:LEKKVKKVCK7BXUESMPL6NVQUTX","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"8bc89ecf7d0dc7ebb3cf9744928296005e3e9070f6a2e173e7fb9cefee768cdb","cross_cats_sorted":["cs.NA","math.NA","math.OC"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-02-04T05:05:43Z","title_canon_sha256":"5b3ca3767a3d766f22e96b3b18b217a646b8133e6a64e68e70ddf340b4ba772a"},"schema_version":"1.0","source":{"id":"2402.02347","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2402.02347","created_at":"2026-07-05T08:27:24Z"},{"alias_kind":"arxiv_version","alias_value":"2402.02347v3","created_at":"2026-07-05T08:27:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.02347","created_at":"2026-07-05T08:27:24Z"},{"alias_kind":"pith_short_12","alias_value":"LEKKVKKVCK7B","created_at":"2026-07-05T08:27:24Z"},{"alias_kind":"pith_short_16","alias_value":"LEKKVKKVCK7BXUES","created_at":"2026-07-05T08:27:24Z"},{"alias_kind":"pith_short_8","alias_value":"LEKKVKKV","created_at":"2026-07-05T08:27:24Z"}],"graph_snapshots":[{"event_id":"sha256:387272ea0a2e738979020d493c258472b80e701e352dce3c3e4ec29aad541954","target":"graph","created_at":"2026-07-05T08:27:24Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2402.02347/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Low-Rank Adaptation (LoRA) emerges as a popular parameter-efficient fine-tuning (PEFT) method, which proposes to freeze pretrained model weights and update an additive low-rank trainable matrix. In this work, we study the enhancement of LoRA training by introducing an $r \\times r$ preconditioner in each gradient step where $r$ is the LoRA rank. We theoretically verify that the proposed preconditioner stabilizes feature learning with LoRA under infinite-width NN setting. Empirically, the implementation of this new preconditioner requires a small change to existing optimizer code and creates vir","authors_text":"Fangzhao Zhang, Mert Pilanci","cross_cats":["cs.NA","math.NA","math.OC"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-02-04T05:05:43Z","title":"Riemannian Preconditioned LoRA for Fine-Tuning Foundation Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.02347","kind":"arxiv","version":3},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:9bd12400481a6d8c7e7678f82f02f441336fcf89d16aa51af0e840de85789e04","target":"record","created_at":"2026-07-05T08:27:24Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"8bc89ecf7d0dc7ebb3cf9744928296005e3e9070f6a2e173e7fb9cefee768cdb","cross_cats_sorted":["cs.NA","math.NA","math.OC"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-02-04T05:05:43Z","title_canon_sha256":"5b3ca3767a3d766f22e96b3b18b217a646b8133e6a64e68e70ddf340b4ba772a"},"schema_version":"1.0","source":{"id":"2402.02347","kind":"arxiv","version":3}},"canonical_sha256":"5914aaa95512be1bd09263d7e6d6149dc287c367cc027943347af8795f4536a4","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"5914aaa95512be1bd09263d7e6d6149dc287c367cc027943347af8795f4536a4","first_computed_at":"2026-07-05T08:27:24.541649Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:27:24.541649Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"YqT50nhayYwVakwpoB0QcKNb9TL9tTdmOPG4s0pMPQOtAo7rG/brVBjyEa65/TQ6oGsh/+UmBj27asu1lWTDCA==","signature_status":"signed_v1","signed_at":"2026-07-05T08:27:24.542089Z","signed_message":"canonical_sha256_bytes"},"source_id":"2402.02347","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:9bd12400481a6d8c7e7678f82f02f441336fcf89d16aa51af0e840de85789e04","sha256:387272ea0a2e738979020d493c258472b80e701e352dce3c3e4ec29aad541954"],"state_sha256":"aeb2ae9f93d4ddd1b298e59b1d166af3e40ef4f025886b7c198234e82bec8760"}