{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:NGEB7FQLCJELYHBEDVGBIKHL6S","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":"d6afea56e3d60e209fa54693d0a8a417a09f92967a9273a5fcd5fd6ed9b20be8","cross_cats_sorted":["cs.CL"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2024-10-01T20:05:51Z","title_canon_sha256":"43a563f1fd65299d5d2009f6d671f61a2a988c54a08614f98d03df7a4ac6b3d1"},"schema_version":"1.0","source":{"id":"2410.01044","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2410.01044","created_at":"2026-07-05T11:21:36Z"},{"alias_kind":"arxiv_version","alias_value":"2410.01044v2","created_at":"2026-07-05T11:21:36Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.01044","created_at":"2026-07-05T11:21:36Z"},{"alias_kind":"pith_short_12","alias_value":"NGEB7FQLCJEL","created_at":"2026-07-05T11:21:36Z"},{"alias_kind":"pith_short_16","alias_value":"NGEB7FQLCJELYHBE","created_at":"2026-07-05T11:21:36Z"},{"alias_kind":"pith_short_8","alias_value":"NGEB7FQL","created_at":"2026-07-05T11:21:36Z"}],"graph_snapshots":[{"event_id":"sha256:b46e11acdd363d52282a8fe34f88bb73e04b8c0b8ccdae91b5826a81a0d0c57d","target":"graph","created_at":"2026-07-05T11:21:36Z","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/2410.01044/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The reasoning steps generated by LLMs might be incomplete, as they mimic logical leaps common in everyday communication found in their pre-training data: underlying rationales are frequently left implicit (unstated). To address this challenge, we introduce RATIONALYST, a model for process-supervision of reasoning based on pre-training on a vast collection of rationale annotations extracted from unlabeled data. We extract 79k rationales from web-scale unlabelled dataset (the Pile) and a combination of reasoning datasets with minimal human intervention. This web-scale pre-training for reasoning ","authors_text":"Andrew Wang, Benjamin Van Durme, Chuyu Liu, Daniel Khashabi, Dongwei Jiang, Guoxuan Wang, Jingyu Zhang, Yining Lu","cross_cats":["cs.CL"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2024-10-01T20:05:51Z","title":"RATIONALYST: Mining Implicit Rationales for Process Supervision of Reasoning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.01044","kind":"arxiv","version":2},"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":"sha256:1725e0ce7b0e0ff303f004bb378fafa788176e8648b1789c2798aecd2f0d23f0","target":"record","created_at":"2026-07-05T11:21:36Z","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":"d6afea56e3d60e209fa54693d0a8a417a09f92967a9273a5fcd5fd6ed9b20be8","cross_cats_sorted":["cs.CL"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2024-10-01T20:05:51Z","title_canon_sha256":"43a563f1fd65299d5d2009f6d671f61a2a988c54a08614f98d03df7a4ac6b3d1"},"schema_version":"1.0","source":{"id":"2410.01044","kind":"arxiv","version":2}},"canonical_sha256":"69881f960b1248bc1c241d4c1428ebf48812d2790a8fb3590846046355a048f3","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"69881f960b1248bc1c241d4c1428ebf48812d2790a8fb3590846046355a048f3","first_computed_at":"2026-07-05T11:21:36.303356Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:21:36.303356Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"oywOgpsG0Tepi66W9ofTtQuFOieBx3HcBUZpWl6Nvs+mXA7vxWbkqzml0WsWTrh2p3UeaVETqQIBNz3IseTaDg==","signature_status":"signed_v1","signed_at":"2026-07-05T11:21:36.303874Z","signed_message":"canonical_sha256_bytes"},"source_id":"2410.01044","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:1725e0ce7b0e0ff303f004bb378fafa788176e8648b1789c2798aecd2f0d23f0","sha256:b46e11acdd363d52282a8fe34f88bb73e04b8c0b8ccdae91b5826a81a0d0c57d"],"state_sha256":"5b9a3abdc994fb3539dc7e60f2c7a2b6371e935ebc55ddadb4efabece8937ae7"}