{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:TIWORS6B3JR433ELNXUHAMPSVM","short_pith_number":"pith:TIWORS6B","schema_version":"1.0","canonical_sha256":"9a2ce8cbc1da63cdec8b6de87031f2ab1d3abba71a96c427efaa8a86b8d4b224","source":{"kind":"arxiv","id":"2506.04065","version":1},"attestation_state":"computed","paper":{"title":"Progressive Mastery: Customized Curriculum Learning with Guided Prompting for Mathematical Reasoning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Changze Lv, Di Liang, Li Miao, Lina Chen, Muling Wu, Qi Qian, Shihan Dou, Tianlong Li, Wenhao Liu, Xiaohua Wang, Xiaoqing Zheng, Xuanjing Huang, Zhenghua Wang, Zhibo Xu, Zisu Huang","submitted_at":"2025-06-04T15:31:46Z","abstract_excerpt":"Large Language Models (LLMs) have achieved remarkable performance across various reasoning tasks, yet post-training is constrained by inefficient sample utilization and inflexible difficulty samples processing. To address these limitations, we propose Customized Curriculum Learning (CCL), a novel framework with two key innovations. First, we introduce model-adaptive difficulty definition that customizes curriculum datasets based on each model's individual capabilities rather than using predefined difficulty metrics. Second, we develop \"Guided Prompting,\" which dynamically reduces sample diffic"},"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":"2506.04065","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-06-04T15:31:46Z","cross_cats_sorted":[],"title_canon_sha256":"57191d949a845c927b0c7c6dc56f2a6fdaa36f2e66a0f7f10ba70e7b713ca91e","abstract_canon_sha256":"249a3f4d3448c5c88526d6975bd38763029c402a2d8267d9b62ce7611f10f31b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:15:56.489115Z","signature_b64":"X6rus9v6bz9PUviZ9Sp4RkO8vaJ6R0ANbww2l3INlyAatofgbSJaLSOBgdx87+do7U3qdkN06nGNnVByW6GCAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9a2ce8cbc1da63cdec8b6de87031f2ab1d3abba71a96c427efaa8a86b8d4b224","last_reissued_at":"2026-07-05T11:15:56.488607Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:15:56.488607Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Progressive Mastery: Customized Curriculum Learning with Guided Prompting for Mathematical Reasoning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Changze Lv, Di Liang, Li Miao, Lina Chen, Muling Wu, Qi Qian, Shihan Dou, Tianlong Li, Wenhao Liu, Xiaohua Wang, Xiaoqing Zheng, Xuanjing Huang, Zhenghua Wang, Zhibo Xu, Zisu Huang","submitted_at":"2025-06-04T15:31:46Z","abstract_excerpt":"Large Language Models (LLMs) have achieved remarkable performance across various reasoning tasks, yet post-training is constrained by inefficient sample utilization and inflexible difficulty samples processing. To address these limitations, we propose Customized Curriculum Learning (CCL), a novel framework with two key innovations. First, we introduce model-adaptive difficulty definition that customizes curriculum datasets based on each model's individual capabilities rather than using predefined difficulty metrics. Second, we develop \"Guided Prompting,\" which dynamically reduces sample diffic"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.04065","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/2506.04065/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":"2506.04065","created_at":"2026-07-05T11:15:56.488688+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.04065v1","created_at":"2026-07-05T11:15:56.488688+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.04065","created_at":"2026-07-05T11:15:56.488688+00:00"},{"alias_kind":"pith_short_12","alias_value":"TIWORS6B3JR4","created_at":"2026-07-05T11:15:56.488688+00:00"},{"alias_kind":"pith_short_16","alias_value":"TIWORS6B3JR433EL","created_at":"2026-07-05T11:15:56.488688+00:00"},{"alias_kind":"pith_short_8","alias_value":"TIWORS6B","created_at":"2026-07-05T11:15:56.488688+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.10079","citing_title":"Why Supervised Fine-Tuning Fails to Learn: A Systematic Study of Incomplete Learning in Large Language Models","ref_index":11,"is_internal_anchor":false},{"citing_arxiv_id":"2604.17051","citing_title":"Efficient Task Adaptation in Large Language Models via Selective Parameter Optimization","ref_index":37,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/TIWORS6B3JR433ELNXUHAMPSVM","json":"https://pith.science/pith/TIWORS6B3JR433ELNXUHAMPSVM.json","graph_json":"https://pith.science/api/pith-number/TIWORS6B3JR433ELNXUHAMPSVM/graph.json","events_json":"https://pith.science/api/pith-number/TIWORS6B3JR433ELNXUHAMPSVM/events.json","paper":"https://pith.science/paper/TIWORS6B"},"agent_actions":{"view_html":"https://pith.science/pith/TIWORS6B3JR433ELNXUHAMPSVM","download_json":"https://pith.science/pith/TIWORS6B3JR433ELNXUHAMPSVM.json","view_paper":"https://pith.science/paper/TIWORS6B","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.04065&json=true","fetch_graph":"https://pith.science/api/pith-number/TIWORS6B3JR433ELNXUHAMPSVM/graph.json","fetch_events":"https://pith.science/api/pith-number/TIWORS6B3JR433ELNXUHAMPSVM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TIWORS6B3JR433ELNXUHAMPSVM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TIWORS6B3JR433ELNXUHAMPSVM/action/storage_attestation","attest_author":"https://pith.science/pith/TIWORS6B3JR433ELNXUHAMPSVM/action/author_attestation","sign_citation":"https://pith.science/pith/TIWORS6B3JR433ELNXUHAMPSVM/action/citation_signature","submit_replication":"https://pith.science/pith/TIWORS6B3JR433ELNXUHAMPSVM/action/replication_record"}},"created_at":"2026-07-05T11:15:56.488688+00:00","updated_at":"2026-07-05T11:15:56.488688+00:00"}