{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:DXSP5NVEERJHRQQZWEVKF5WFKC","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":"f961797764d2159cb317f605409b76ee6d3d8365417ad2387fa584332d3241da","cross_cats_sorted":["cs.AI","cs.CL"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-05-28T17:22:22Z","title_canon_sha256":"d0a02d09f67b69d1350036aec011ca1386a84dddd226ccf84f57c55e932fe3d6"},"schema_version":"1.0","source":{"id":"2405.18380","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2405.18380","created_at":"2026-07-05T11:18:12Z"},{"alias_kind":"arxiv_version","alias_value":"2405.18380v3","created_at":"2026-07-05T11:18:12Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.18380","created_at":"2026-07-05T11:18:12Z"},{"alias_kind":"pith_short_12","alias_value":"DXSP5NVEERJH","created_at":"2026-07-05T11:18:12Z"},{"alias_kind":"pith_short_16","alias_value":"DXSP5NVEERJHRQQZ","created_at":"2026-07-05T11:18:12Z"},{"alias_kind":"pith_short_8","alias_value":"DXSP5NVE","created_at":"2026-07-05T11:18:12Z"}],"graph_snapshots":[{"event_id":"sha256:96a236b06c27af2cae8ddc7bd5d250817f1e9eb69a4f1f0c24c2dc2eb59e2c18","target":"graph","created_at":"2026-07-05T11:18:12Z","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/2405.18380/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The rapid advancements in Large Language Models (LLMs) have revolutionized various natural language processing tasks. However, the substantial size of LLMs presents significant challenges in training or fine-tuning. While parameter-efficient approaches such as low-rank adaptation (LoRA) have gained popularity, they often compromise performance compared to full-rank fine-tuning. In this paper, we propose Outlier-weighed Layerwise Sampling (OWS), a new memory-efficient fine-tuning approach, inspired by the layerwise outlier distribution of LLMs. Unlike LoRA, which adds extra adapters to all laye","authors_text":"Lu Yin, Pengxiang Li, Shiwei Liu, Xiaowei Gao","cross_cats":["cs.AI","cs.CL"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-05-28T17:22:22Z","title":"Outlier-weighed Layerwise Sampling for LLM Fine-tuning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.18380","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:f3ba2afbb23dbdfa9815253197c36eea863a8fe3c237339278bb098ca010a205","target":"record","created_at":"2026-07-05T11:18:12Z","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":"f961797764d2159cb317f605409b76ee6d3d8365417ad2387fa584332d3241da","cross_cats_sorted":["cs.AI","cs.CL"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-05-28T17:22:22Z","title_canon_sha256":"d0a02d09f67b69d1350036aec011ca1386a84dddd226ccf84f57c55e932fe3d6"},"schema_version":"1.0","source":{"id":"2405.18380","kind":"arxiv","version":3}},"canonical_sha256":"1de4feb6a4245278c219b12aa2f6c5508189a12c4b8b44279dba6123b7483b0d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"1de4feb6a4245278c219b12aa2f6c5508189a12c4b8b44279dba6123b7483b0d","first_computed_at":"2026-07-05T11:18:12.422628Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:18:12.422628Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"RMFLjdql2HWjd+HKRl4yrMEobwwUfyhF4wiXIOt1OTu3By1fl0pGHoDj1yrsI6StthNNbdMdqc9qtbcJxKATDQ==","signature_status":"signed_v1","signed_at":"2026-07-05T11:18:12.423080Z","signed_message":"canonical_sha256_bytes"},"source_id":"2405.18380","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f3ba2afbb23dbdfa9815253197c36eea863a8fe3c237339278bb098ca010a205","sha256:96a236b06c27af2cae8ddc7bd5d250817f1e9eb69a4f1f0c24c2dc2eb59e2c18"],"state_sha256":"2979f14aa50b05503e6b421e22b4e7fe84226a1c54938bc937e6d0e3aad8e134"}