{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:V6SDHWEEDSRVS74MO6ZLR5PDFW","short_pith_number":"pith:V6SDHWEE","canonical_record":{"source":{"id":"2402.02314","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-02-04T01:55:00Z","cross_cats_sorted":["cs.AI","cs.CL"],"title_canon_sha256":"4b199e5265f38f6a21ee9e88a62add5925bbfa06474bf6d43325ec8c01193acb","abstract_canon_sha256":"1b550ce9718eadf0721f09c485e65fb11a125cced00d318d90884750770fe122"},"schema_version":"1.0"},"canonical_sha256":"afa433d8841ca3597f8c77b2b8f5e32d8a7aa82f167399b84337e2dac05dfe7b","source":{"kind":"arxiv","id":"2402.02314","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2402.02314","created_at":"2026-07-05T08:24:00Z"},{"alias_kind":"arxiv_version","alias_value":"2402.02314v3","created_at":"2026-07-05T08:24:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.02314","created_at":"2026-07-05T08:24:00Z"},{"alias_kind":"pith_short_12","alias_value":"V6SDHWEEDSRV","created_at":"2026-07-05T08:24:00Z"},{"alias_kind":"pith_short_16","alias_value":"V6SDHWEEDSRVS74M","created_at":"2026-07-05T08:24:00Z"},{"alias_kind":"pith_short_8","alias_value":"V6SDHWEE","created_at":"2026-07-05T08:24:00Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:V6SDHWEEDSRVS74MO6ZLR5PDFW","target":"record","payload":{"canonical_record":{"source":{"id":"2402.02314","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-02-04T01:55:00Z","cross_cats_sorted":["cs.AI","cs.CL"],"title_canon_sha256":"4b199e5265f38f6a21ee9e88a62add5925bbfa06474bf6d43325ec8c01193acb","abstract_canon_sha256":"1b550ce9718eadf0721f09c485e65fb11a125cced00d318d90884750770fe122"},"schema_version":"1.0"},"canonical_sha256":"afa433d8841ca3597f8c77b2b8f5e32d8a7aa82f167399b84337e2dac05dfe7b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:24:00.706855Z","signature_b64":"AMHTm+u/eLtzxow76Po/eK+L1501k2wlLdk+jGfnhkgpOnL0c1LzT922/uume3kSNhjIeNzwxBl4WF+h6brvBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"afa433d8841ca3597f8c77b2b8f5e32d8a7aa82f167399b84337e2dac05dfe7b","last_reissued_at":"2026-07-05T08:24:00.706346Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:24:00.706346Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2402.02314","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T08:24:00Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"seXnuG3+zEQw3p55JhafIo6cVIXI5zDYNFB3LVVC3dLRPA0pNsXFib16sIMyWDLI7bRMr3/ZA/8E+pSbB8ubCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-11T13:24:10.958788Z"},"content_sha256":"b9ab80cc43e9800245fa5ac4a4858d796e30bbaae1dd117175f1d4ff118dfaf5","schema_version":"1.0","event_id":"sha256:b9ab80cc43e9800245fa5ac4a4858d796e30bbaae1dd117175f1d4ff118dfaf5"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:V6SDHWEEDSRVS74MO6ZLR5PDFW","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Selecting Large Language Model to Fine-tune via Rectified Scaling Law","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CL"],"primary_cat":"cs.LG","authors_text":"Baizhou Huang, Haotian Ye, Haowei Lin, James Zou, Jianzhu Ma, Qinyu Chen, Sujian Li, Xiaojun Wan, Yitao Liang, Zihao Wang","submitted_at":"2024-02-04T01:55:00Z","abstract_excerpt":"The ever-growing ecosystem of LLMs has posed a challenge in selecting the most appropriate pre-trained model to fine-tune amidst a sea of options. Given constrained resources, fine-tuning all models and making selections afterward is unrealistic. In this work, we formulate this resource-constrained selection task into predicting fine-tuning performance and illustrate its natural connection with Scaling Law. Unlike pre-training, we find that the fine-tuning scaling curve includes not just the well-known \"power phase\" but also the previously unobserved \"pre-power phase\". We also explain why exis"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.02314","kind":"arxiv","version":3},"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/2402.02314/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T08:24:00Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"xmBn2tUrf4VhCtlPUfEkchRyjcmvho4I0sPl3T9hKgUFwft0wS/ayO784YIluvXlwQWtcZazah7wLwz/KizbBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-11T13:24:10.959321Z"},"content_sha256":"baa948bada47d0cac4d7389c4e1b0957fafc8426eb16cfef306f3b4663b21238","schema_version":"1.0","event_id":"sha256:baa948bada47d0cac4d7389c4e1b0957fafc8426eb16cfef306f3b4663b21238"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/V6SDHWEEDSRVS74MO6ZLR5PDFW/bundle.json","state_url":"https://pith.science/pith/V6SDHWEEDSRVS74MO6ZLR5PDFW/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/V6SDHWEEDSRVS74MO6ZLR5PDFW/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-11T13:24:10Z","links":{"resolver":"https://pith.science/pith/V6SDHWEEDSRVS74MO6ZLR5PDFW","bundle":"https://pith.science/pith/V6SDHWEEDSRVS74MO6ZLR5PDFW/bundle.json","state":"https://pith.science/pith/V6SDHWEEDSRVS74MO6ZLR5PDFW/state.json","well_known_bundle":"https://pith.science/.well-known/pith/V6SDHWEEDSRVS74MO6ZLR5PDFW/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:V6SDHWEEDSRVS74MO6ZLR5PDFW","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":"1b550ce9718eadf0721f09c485e65fb11a125cced00d318d90884750770fe122","cross_cats_sorted":["cs.AI","cs.CL"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-02-04T01:55:00Z","title_canon_sha256":"4b199e5265f38f6a21ee9e88a62add5925bbfa06474bf6d43325ec8c01193acb"},"schema_version":"1.0","source":{"id":"2402.02314","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2402.02314","created_at":"2026-07-05T08:24:00Z"},{"alias_kind":"arxiv_version","alias_value":"2402.02314v3","created_at":"2026-07-05T08:24:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.02314","created_at":"2026-07-05T08:24:00Z"},{"alias_kind":"pith_short_12","alias_value":"V6SDHWEEDSRV","created_at":"2026-07-05T08:24:00Z"},{"alias_kind":"pith_short_16","alias_value":"V6SDHWEEDSRVS74M","created_at":"2026-07-05T08:24:00Z"},{"alias_kind":"pith_short_8","alias_value":"V6SDHWEE","created_at":"2026-07-05T08:24:00Z"}],"graph_snapshots":[{"event_id":"sha256:baa948bada47d0cac4d7389c4e1b0957fafc8426eb16cfef306f3b4663b21238","target":"graph","created_at":"2026-07-05T08:24:00Z","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/2402.02314/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The ever-growing ecosystem of LLMs has posed a challenge in selecting the most appropriate pre-trained model to fine-tune amidst a sea of options. Given constrained resources, fine-tuning all models and making selections afterward is unrealistic. In this work, we formulate this resource-constrained selection task into predicting fine-tuning performance and illustrate its natural connection with Scaling Law. Unlike pre-training, we find that the fine-tuning scaling curve includes not just the well-known \"power phase\" but also the previously unobserved \"pre-power phase\". We also explain why exis","authors_text":"Baizhou Huang, Haotian Ye, Haowei Lin, James Zou, Jianzhu Ma, Qinyu Chen, Sujian Li, Xiaojun Wan, Yitao Liang, Zihao Wang","cross_cats":["cs.AI","cs.CL"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-02-04T01:55:00Z","title":"Selecting Large Language Model to Fine-tune via Rectified Scaling Law"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.02314","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:b9ab80cc43e9800245fa5ac4a4858d796e30bbaae1dd117175f1d4ff118dfaf5","target":"record","created_at":"2026-07-05T08:24:00Z","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":"1b550ce9718eadf0721f09c485e65fb11a125cced00d318d90884750770fe122","cross_cats_sorted":["cs.AI","cs.CL"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-02-04T01:55:00Z","title_canon_sha256":"4b199e5265f38f6a21ee9e88a62add5925bbfa06474bf6d43325ec8c01193acb"},"schema_version":"1.0","source":{"id":"2402.02314","kind":"arxiv","version":3}},"canonical_sha256":"afa433d8841ca3597f8c77b2b8f5e32d8a7aa82f167399b84337e2dac05dfe7b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"afa433d8841ca3597f8c77b2b8f5e32d8a7aa82f167399b84337e2dac05dfe7b","first_computed_at":"2026-07-05T08:24:00.706346Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:24:00.706346Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"AMHTm+u/eLtzxow76Po/eK+L1501k2wlLdk+jGfnhkgpOnL0c1LzT922/uume3kSNhjIeNzwxBl4WF+h6brvBQ==","signature_status":"signed_v1","signed_at":"2026-07-05T08:24:00.706855Z","signed_message":"canonical_sha256_bytes"},"source_id":"2402.02314","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b9ab80cc43e9800245fa5ac4a4858d796e30bbaae1dd117175f1d4ff118dfaf5","sha256:baa948bada47d0cac4d7389c4e1b0957fafc8426eb16cfef306f3b4663b21238"],"state_sha256":"36686094fad20b3d9bc700e732a7e18fe9d0abb6d9f621251a42adabab98e7f0"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"s3Cjk6O7oBdr8iLNz164yQHDvbkYsA0OVmph8R3ZzZRKiVnRyutbXlbsSBEhvlA7Zz4GQ7D+6p5oeCQFpyhgBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-11T13:24:10.963570Z","bundle_sha256":"bb753c3ce77bca17c622e45662e2cd6bedc294232ce5116e85b83fa033474b04"}}