{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:SLEXJZMMAPDWPQXSBIBKOOCB5I","short_pith_number":"pith:SLEXJZMM","schema_version":"1.0","canonical_sha256":"92c974e58c03c767c2f20a02a73841ea0e0d562013d75b0b7a9670b432f66957","source":{"kind":"arxiv","id":"2405.16064","version":1},"attestation_state":"computed","paper":{"title":"Keypoint-based Progressive Chain-of-Thought Distillation for LLMs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Changsheng Li, Guoren Wang, Jun Zhou, Kaituo Feng, Xiaolu Zhang, Ye Yuan","submitted_at":"2024-05-25T05:27:38Z","abstract_excerpt":"Chain-of-thought distillation is a powerful technique for transferring reasoning abilities from large language models (LLMs) to smaller student models. Previous methods typically require the student to mimic the step-by-step rationale produced by LLMs, often facing the following challenges: (i) Tokens within a rationale vary in significance, and treating them equally may fail to accurately mimic keypoint tokens, leading to reasoning errors. (ii) They usually distill knowledge by consistently predicting all the steps in a rationale, which falls short in distinguishing the learning order of step"},"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":"2405.16064","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-05-25T05:27:38Z","cross_cats_sorted":[],"title_canon_sha256":"66c8f54478b505dc5339934d4ec5917c604802f47d3f32a8d0c83a9b3c7c0af2","abstract_canon_sha256":"b7be174a13403a44861d840da8aa27628f8206a97bea2fcf00c73492d24321c2"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:23:25.159187Z","signature_b64":"nUgR1RWX68p9EMfAJbFX5iPPu5KoIV2Yqs27C+MdUFyJGAt70TaqpS717sASsNVr6Ic25fNUbi08dTTh4kwoBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"92c974e58c03c767c2f20a02a73841ea0e0d562013d75b0b7a9670b432f66957","last_reissued_at":"2026-07-05T08:23:25.158749Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:23:25.158749Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Keypoint-based Progressive Chain-of-Thought Distillation for LLMs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Changsheng Li, Guoren Wang, Jun Zhou, Kaituo Feng, Xiaolu Zhang, Ye Yuan","submitted_at":"2024-05-25T05:27:38Z","abstract_excerpt":"Chain-of-thought distillation is a powerful technique for transferring reasoning abilities from large language models (LLMs) to smaller student models. Previous methods typically require the student to mimic the step-by-step rationale produced by LLMs, often facing the following challenges: (i) Tokens within a rationale vary in significance, and treating them equally may fail to accurately mimic keypoint tokens, leading to reasoning errors. (ii) They usually distill knowledge by consistently predicting all the steps in a rationale, which falls short in distinguishing the learning order of step"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.16064","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/2405.16064/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":"2405.16064","created_at":"2026-07-05T08:23:25.158805+00:00"},{"alias_kind":"arxiv_version","alias_value":"2405.16064v1","created_at":"2026-07-05T08:23:25.158805+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.16064","created_at":"2026-07-05T08:23:25.158805+00:00"},{"alias_kind":"pith_short_12","alias_value":"SLEXJZMMAPDW","created_at":"2026-07-05T08:23:25.158805+00:00"},{"alias_kind":"pith_short_16","alias_value":"SLEXJZMMAPDWPQXS","created_at":"2026-07-05T08:23:25.158805+00:00"},{"alias_kind":"pith_short_8","alias_value":"SLEXJZMM","created_at":"2026-07-05T08:23:25.158805+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":4,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.22830","citing_title":"Finding the Evidence: Discovering Decision-Supporting Tokens for On-Policy Reasoning Distillation","ref_index":4,"is_internal_anchor":false},{"citing_arxiv_id":"2606.06840","citing_title":"Characterize Then Distill: Mechanistic Reasoning in Large Output Spaces","ref_index":24,"is_internal_anchor":false},{"citing_arxiv_id":"2606.06840","citing_title":"Characterize Then Distill: Mechanistic Reasoning in Large Output Spaces","ref_index":78,"is_internal_anchor":false},{"citing_arxiv_id":"2503.21776","citing_title":"Video-R1: Reinforcing Video Reasoning in MLLMs","ref_index":8,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/SLEXJZMMAPDWPQXSBIBKOOCB5I","json":"https://pith.science/pith/SLEXJZMMAPDWPQXSBIBKOOCB5I.json","graph_json":"https://pith.science/api/pith-number/SLEXJZMMAPDWPQXSBIBKOOCB5I/graph.json","events_json":"https://pith.science/api/pith-number/SLEXJZMMAPDWPQXSBIBKOOCB5I/events.json","paper":"https://pith.science/paper/SLEXJZMM"},"agent_actions":{"view_html":"https://pith.science/pith/SLEXJZMMAPDWPQXSBIBKOOCB5I","download_json":"https://pith.science/pith/SLEXJZMMAPDWPQXSBIBKOOCB5I.json","view_paper":"https://pith.science/paper/SLEXJZMM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2405.16064&json=true","fetch_graph":"https://pith.science/api/pith-number/SLEXJZMMAPDWPQXSBIBKOOCB5I/graph.json","fetch_events":"https://pith.science/api/pith-number/SLEXJZMMAPDWPQXSBIBKOOCB5I/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SLEXJZMMAPDWPQXSBIBKOOCB5I/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SLEXJZMMAPDWPQXSBIBKOOCB5I/action/storage_attestation","attest_author":"https://pith.science/pith/SLEXJZMMAPDWPQXSBIBKOOCB5I/action/author_attestation","sign_citation":"https://pith.science/pith/SLEXJZMMAPDWPQXSBIBKOOCB5I/action/citation_signature","submit_replication":"https://pith.science/pith/SLEXJZMMAPDWPQXSBIBKOOCB5I/action/replication_record"}},"created_at":"2026-07-05T08:23:25.158805+00:00","updated_at":"2026-07-05T08:23:25.158805+00:00"}