{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:6L5DHGZOKVRGTJ5ZHTJ3GPSDAY","short_pith_number":"pith:6L5DHGZO","schema_version":"1.0","canonical_sha256":"f2fa339b2e556269a7b93cd3b33e4306036b66b33b83c7019729bd1cdad9ad43","source":{"kind":"arxiv","id":"2506.13358","version":1},"attestation_state":"computed","paper":{"title":"Socratic RL: A Novel Framework for Efficient Knowledge Acquisition through Iterative Reflection and Viewpoint Distillation","license":"http://creativecommons.org/publicdomain/zero/1.0/","headline":"","cross_cats":["cs.LG","cs.MA"],"primary_cat":"cs.AI","authors_text":"Xiangfan Wu","submitted_at":"2025-06-16T10:57:58Z","abstract_excerpt":"Current Reinforcement Learning (RL) methodologies for Large Language Models (LLMs) often rely on simplistic, outcome-based reward signals (e.g., final answer correctness), which limits the depth of learning from each interaction. This paper introduces Socratic Reinforcement Learning (Socratic-RL), a novel, process-oriented framework designed to address this limitation. Socratic-RL operates on the principle that deeper understanding is achieved by reflecting on the causal reasons for errors and successes within the reasoning process itself. The framework employs a decoupled \"Teacher-Student\" ar"},"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":"2506.13358","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.AI","submitted_at":"2025-06-16T10:57:58Z","cross_cats_sorted":["cs.LG","cs.MA"],"title_canon_sha256":"71da39286d629a45123c9e8f4b752a3d2f8158efeeedc6c129127feb1d619a41","abstract_canon_sha256":"debf57d3c81696e6301812a09135024a1d84fd3bb33fb5b5ff6f33b229e59033"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:22:09.212534Z","signature_b64":"fddHzQL9r/RhicZ0pfXUCsZqFRYblut8FmwbGNWUUVZCp9V+IgEFt5654JmwiBFsiMjz/04gbc0k4KlviZh/DQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f2fa339b2e556269a7b93cd3b33e4306036b66b33b83c7019729bd1cdad9ad43","last_reissued_at":"2026-07-05T11:22:09.212071Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:22:09.212071Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Socratic RL: A Novel Framework for Efficient Knowledge Acquisition through Iterative Reflection and Viewpoint Distillation","license":"http://creativecommons.org/publicdomain/zero/1.0/","headline":"","cross_cats":["cs.LG","cs.MA"],"primary_cat":"cs.AI","authors_text":"Xiangfan Wu","submitted_at":"2025-06-16T10:57:58Z","abstract_excerpt":"Current Reinforcement Learning (RL) methodologies for Large Language Models (LLMs) often rely on simplistic, outcome-based reward signals (e.g., final answer correctness), which limits the depth of learning from each interaction. This paper introduces Socratic Reinforcement Learning (Socratic-RL), a novel, process-oriented framework designed to address this limitation. Socratic-RL operates on the principle that deeper understanding is achieved by reflecting on the causal reasons for errors and successes within the reasoning process itself. The framework employs a decoupled \"Teacher-Student\" ar"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.13358","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/2506.13358/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":"2506.13358","created_at":"2026-07-05T11:22:09.212127+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.13358v1","created_at":"2026-07-05T11:22:09.212127+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.13358","created_at":"2026-07-05T11:22:09.212127+00:00"},{"alias_kind":"pith_short_12","alias_value":"6L5DHGZOKVRG","created_at":"2026-07-05T11:22:09.212127+00:00"},{"alias_kind":"pith_short_16","alias_value":"6L5DHGZOKVRGTJ5Z","created_at":"2026-07-05T11:22:09.212127+00:00"},{"alias_kind":"pith_short_8","alias_value":"6L5DHGZO","created_at":"2026-07-05T11:22:09.212127+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/6L5DHGZOKVRGTJ5ZHTJ3GPSDAY","json":"https://pith.science/pith/6L5DHGZOKVRGTJ5ZHTJ3GPSDAY.json","graph_json":"https://pith.science/api/pith-number/6L5DHGZOKVRGTJ5ZHTJ3GPSDAY/graph.json","events_json":"https://pith.science/api/pith-number/6L5DHGZOKVRGTJ5ZHTJ3GPSDAY/events.json","paper":"https://pith.science/paper/6L5DHGZO"},"agent_actions":{"view_html":"https://pith.science/pith/6L5DHGZOKVRGTJ5ZHTJ3GPSDAY","download_json":"https://pith.science/pith/6L5DHGZOKVRGTJ5ZHTJ3GPSDAY.json","view_paper":"https://pith.science/paper/6L5DHGZO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.13358&json=true","fetch_graph":"https://pith.science/api/pith-number/6L5DHGZOKVRGTJ5ZHTJ3GPSDAY/graph.json","fetch_events":"https://pith.science/api/pith-number/6L5DHGZOKVRGTJ5ZHTJ3GPSDAY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/6L5DHGZOKVRGTJ5ZHTJ3GPSDAY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/6L5DHGZOKVRGTJ5ZHTJ3GPSDAY/action/storage_attestation","attest_author":"https://pith.science/pith/6L5DHGZOKVRGTJ5ZHTJ3GPSDAY/action/author_attestation","sign_citation":"https://pith.science/pith/6L5DHGZOKVRGTJ5ZHTJ3GPSDAY/action/citation_signature","submit_replication":"https://pith.science/pith/6L5DHGZOKVRGTJ5ZHTJ3GPSDAY/action/replication_record"}},"created_at":"2026-07-05T11:22:09.212127+00:00","updated_at":"2026-07-05T11:22:09.212127+00:00"}