{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:Z5YYX4EYHF453MKQ2LGFIWENVO","short_pith_number":"pith:Z5YYX4EY","schema_version":"1.0","canonical_sha256":"cf718bf0983979ddb150d2cc54588daba99611c3f6aacec8b4f451401004690c","source":{"kind":"arxiv","id":"2402.16141","version":1},"attestation_state":"computed","paper":{"title":"PeriodicLoRA: Breaking the Low-Rank Bottleneck in LoRA Optimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Damai Dai, Liang Chen, Peiyi Wang, Qingxiu Dong, Shaoxiang Wu, Weiyao Luo, Xiangdi Meng, Xiaochen Wang, Zhe Yang, Zhifang Sui","submitted_at":"2024-02-25T16:43:41Z","abstract_excerpt":"Supervised fine-tuning is the most common method to adapt large language models (LLMs) to downstream tasks, but full fine-tuning LLMs requires massive computational resources. Recently, parameter-efficient fine-tuning (PEFT) methods have been widely studied due to its cost-effectiveness. LoRA is one of the most widely used methods, which assumes that the optimization process is essentially low-dimensional. Although LoRA fine-tuning is effective, there is still a performance gap compared to full fine-tuning, since its weight update is limited to low-rank matrices. In order to break the low-rank"},"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":"2402.16141","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-02-25T16:43:41Z","cross_cats_sorted":[],"title_canon_sha256":"0ba98a4040a5bf8901eb093c808403745dfa8138c9ac5a1ea4a9831b34b47b73","abstract_canon_sha256":"3fa72d1f7c21bc06f9ddb215cb1aac49098fd50ee51cfd40f2590d9618464fd2"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:49:02.767009Z","signature_b64":"okwQxvEdcf5Y/XP7Ahfx2hs0FV1MBuWSKba/BCYKHqcv31R+FSPj3qsTBlXEhw7hvjZqbLe0SIFbX7XcOHEDCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cf718bf0983979ddb150d2cc54588daba99611c3f6aacec8b4f451401004690c","last_reissued_at":"2026-07-05T07:49:02.766579Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:49:02.766579Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"PeriodicLoRA: Breaking the Low-Rank Bottleneck in LoRA Optimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Damai Dai, Liang Chen, Peiyi Wang, Qingxiu Dong, Shaoxiang Wu, Weiyao Luo, Xiangdi Meng, Xiaochen Wang, Zhe Yang, Zhifang Sui","submitted_at":"2024-02-25T16:43:41Z","abstract_excerpt":"Supervised fine-tuning is the most common method to adapt large language models (LLMs) to downstream tasks, but full fine-tuning LLMs requires massive computational resources. Recently, parameter-efficient fine-tuning (PEFT) methods have been widely studied due to its cost-effectiveness. LoRA is one of the most widely used methods, which assumes that the optimization process is essentially low-dimensional. Although LoRA fine-tuning is effective, there is still a performance gap compared to full fine-tuning, since its weight update is limited to low-rank matrices. In order to break the low-rank"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.16141","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/2402.16141/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":"2402.16141","created_at":"2026-07-05T07:49:02.766640+00:00"},{"alias_kind":"arxiv_version","alias_value":"2402.16141v1","created_at":"2026-07-05T07:49:02.766640+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.16141","created_at":"2026-07-05T07:49:02.766640+00:00"},{"alias_kind":"pith_short_12","alias_value":"Z5YYX4EYHF45","created_at":"2026-07-05T07:49:02.766640+00:00"},{"alias_kind":"pith_short_16","alias_value":"Z5YYX4EYHF453MKQ","created_at":"2026-07-05T07:49:02.766640+00:00"},{"alias_kind":"pith_short_8","alias_value":"Z5YYX4EY","created_at":"2026-07-05T07:49:02.766640+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.12883","citing_title":"The Hidden Power of Scaling Factor in LoRA Optimization","ref_index":33,"is_internal_anchor":false},{"citing_arxiv_id":"2606.07404","citing_title":"Reversible Foundations: Training a 120B Sparse MoE through State-Preserving Scaling","ref_index":52,"is_internal_anchor":false},{"citing_arxiv_id":"2403.14608","citing_title":"Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey","ref_index":89,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/Z5YYX4EYHF453MKQ2LGFIWENVO","json":"https://pith.science/pith/Z5YYX4EYHF453MKQ2LGFIWENVO.json","graph_json":"https://pith.science/api/pith-number/Z5YYX4EYHF453MKQ2LGFIWENVO/graph.json","events_json":"https://pith.science/api/pith-number/Z5YYX4EYHF453MKQ2LGFIWENVO/events.json","paper":"https://pith.science/paper/Z5YYX4EY"},"agent_actions":{"view_html":"https://pith.science/pith/Z5YYX4EYHF453MKQ2LGFIWENVO","download_json":"https://pith.science/pith/Z5YYX4EYHF453MKQ2LGFIWENVO.json","view_paper":"https://pith.science/paper/Z5YYX4EY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2402.16141&json=true","fetch_graph":"https://pith.science/api/pith-number/Z5YYX4EYHF453MKQ2LGFIWENVO/graph.json","fetch_events":"https://pith.science/api/pith-number/Z5YYX4EYHF453MKQ2LGFIWENVO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/Z5YYX4EYHF453MKQ2LGFIWENVO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/Z5YYX4EYHF453MKQ2LGFIWENVO/action/storage_attestation","attest_author":"https://pith.science/pith/Z5YYX4EYHF453MKQ2LGFIWENVO/action/author_attestation","sign_citation":"https://pith.science/pith/Z5YYX4EYHF453MKQ2LGFIWENVO/action/citation_signature","submit_replication":"https://pith.science/pith/Z5YYX4EYHF453MKQ2LGFIWENVO/action/replication_record"}},"created_at":"2026-07-05T07:49:02.766640+00:00","updated_at":"2026-07-05T07:49:02.766640+00:00"}