{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:FRM66FPS27DSFTQLM47QHZSHMO","short_pith_number":"pith:FRM66FPS","schema_version":"1.0","canonical_sha256":"2c59ef15f2d7c722ce0b673f03e647638e30f17461423513fb2aba46adef90b1","source":{"kind":"arxiv","id":"2411.14698","version":1},"attestation_state":"computed","paper":{"title":"Improving Mathematical Reasoning Capabilities of Small Language Models via Feedback-Driven Distillation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Can Ma, Jian Li, Weiping Wang, Xunyu Zhu","submitted_at":"2024-11-22T03:12:39Z","abstract_excerpt":"Large Language Models (LLMs) demonstrate exceptional reasoning capabilities, often achieving state-of-the-art performance in various tasks. However, their substantial computational and memory demands, due to billions of parameters, hinder deployment in resource-constrained environments. A promising solution is knowledge distillation, where LLMs transfer reasoning capabilities to Small Language Models (SLMs, $\\le$ 1B parameters), enabling wider deployment on low-resource devices. Existing methods primarily focus on generating high-quality reasoning rationales for distillation datasets but often"},"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":"2411.14698","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-11-22T03:12:39Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"275ce26e871451fa9df5eec455785b0c1996520db7eb6c7d68e76ef59bb4f6f6","abstract_canon_sha256":"7671d51d5bacf27d5bd9dea4299be24bcdcf6fbeb9b743797a3f84d056edcd40"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:38:55.738167Z","signature_b64":"XZzlism62ExBQukfS5Jr1KerT1E+l5JhOu2HvkRo0TxEPwFZ+qnJ6KQ+SVgDPm6xrYHqWZQ14w0KmqMRsbycAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2c59ef15f2d7c722ce0b673f03e647638e30f17461423513fb2aba46adef90b1","last_reissued_at":"2026-07-05T09:38:55.737676Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:38:55.737676Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Improving Mathematical Reasoning Capabilities of Small Language Models via Feedback-Driven Distillation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Can Ma, Jian Li, Weiping Wang, Xunyu Zhu","submitted_at":"2024-11-22T03:12:39Z","abstract_excerpt":"Large Language Models (LLMs) demonstrate exceptional reasoning capabilities, often achieving state-of-the-art performance in various tasks. However, their substantial computational and memory demands, due to billions of parameters, hinder deployment in resource-constrained environments. A promising solution is knowledge distillation, where LLMs transfer reasoning capabilities to Small Language Models (SLMs, $\\le$ 1B parameters), enabling wider deployment on low-resource devices. Existing methods primarily focus on generating high-quality reasoning rationales for distillation datasets but often"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.14698","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/2411.14698/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":"2411.14698","created_at":"2026-07-05T09:38:55.737742+00:00"},{"alias_kind":"arxiv_version","alias_value":"2411.14698v1","created_at":"2026-07-05T09:38:55.737742+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.14698","created_at":"2026-07-05T09:38:55.737742+00:00"},{"alias_kind":"pith_short_12","alias_value":"FRM66FPS27DS","created_at":"2026-07-05T09:38:55.737742+00:00"},{"alias_kind":"pith_short_16","alias_value":"FRM66FPS27DSFTQL","created_at":"2026-07-05T09:38:55.737742+00:00"},{"alias_kind":"pith_short_8","alias_value":"FRM66FPS","created_at":"2026-07-05T09:38:55.737742+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.01532","citing_title":"Rethinking the Role of Positional Encoding: Sliding-Window Transformers without PE Remain Turing Complete","ref_index":94,"is_internal_anchor":false},{"citing_arxiv_id":"2503.16419","citing_title":"Stop Overthinking: A Survey on Efficient Reasoning for Large Language Models","ref_index":249,"is_internal_anchor":false},{"citing_arxiv_id":"2605.06165","citing_title":"Post Reasoning: Improving the Performance of Non-Thinking Models at No Cost","ref_index":100,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/FRM66FPS27DSFTQLM47QHZSHMO","json":"https://pith.science/pith/FRM66FPS27DSFTQLM47QHZSHMO.json","graph_json":"https://pith.science/api/pith-number/FRM66FPS27DSFTQLM47QHZSHMO/graph.json","events_json":"https://pith.science/api/pith-number/FRM66FPS27DSFTQLM47QHZSHMO/events.json","paper":"https://pith.science/paper/FRM66FPS"},"agent_actions":{"view_html":"https://pith.science/pith/FRM66FPS27DSFTQLM47QHZSHMO","download_json":"https://pith.science/pith/FRM66FPS27DSFTQLM47QHZSHMO.json","view_paper":"https://pith.science/paper/FRM66FPS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2411.14698&json=true","fetch_graph":"https://pith.science/api/pith-number/FRM66FPS27DSFTQLM47QHZSHMO/graph.json","fetch_events":"https://pith.science/api/pith-number/FRM66FPS27DSFTQLM47QHZSHMO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FRM66FPS27DSFTQLM47QHZSHMO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FRM66FPS27DSFTQLM47QHZSHMO/action/storage_attestation","attest_author":"https://pith.science/pith/FRM66FPS27DSFTQLM47QHZSHMO/action/author_attestation","sign_citation":"https://pith.science/pith/FRM66FPS27DSFTQLM47QHZSHMO/action/citation_signature","submit_replication":"https://pith.science/pith/FRM66FPS27DSFTQLM47QHZSHMO/action/replication_record"}},"created_at":"2026-07-05T09:38:55.737742+00:00","updated_at":"2026-07-05T09:38:55.737742+00:00"}