{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:64XYUXPPLG36URFBVR66FXM523","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":"c4638d47942c89e3f8858d0115140cfe621e38f0b6b4b2c993c77b4016ebb87b","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-08-29T06:49:20Z","title_canon_sha256":"3065d82db88c779a0a3fab9e52304d6d312e8a38bf699a79327d62d606380dcc"},"schema_version":"1.0","source":{"id":"2408.16293","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2408.16293","created_at":"2026-07-05T09:00:28Z"},{"alias_kind":"arxiv_version","alias_value":"2408.16293v1","created_at":"2026-07-05T09:00:28Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2408.16293","created_at":"2026-07-05T09:00:28Z"},{"alias_kind":"pith_short_12","alias_value":"64XYUXPPLG36","created_at":"2026-07-05T09:00:28Z"},{"alias_kind":"pith_short_16","alias_value":"64XYUXPPLG36URFB","created_at":"2026-07-05T09:00:28Z"},{"alias_kind":"pith_short_8","alias_value":"64XYUXPP","created_at":"2026-07-05T09:00:28Z"}],"graph_snapshots":[{"event_id":"sha256:0718f2b5659e9954dbd2f5cd0cff412b2bcea3380b0e39ab32160fb4a288e25f","target":"graph","created_at":"2026-07-05T09:00:28Z","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/2408.16293/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Language models have demonstrated remarkable performance in solving reasoning tasks; however, even the strongest models still occasionally make reasoning mistakes. Recently, there has been active research aimed at improving reasoning accuracy, particularly by using pretrained language models to \"self-correct\" their mistakes via multi-round prompting. In this paper, we follow this line of work but focus on understanding the usefulness of incorporating \"error-correction\" data directly into the pretraining stage. This data consists of erroneous solution steps immediately followed by their correct","authors_text":"Tian Ye, Yuanzhi Li, Zeyuan Allen-Zhu, Zicheng Xu","cross_cats":["cs.AI","cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-08-29T06:49:20Z","title":"Physics of Language Models: Part 2.2, How to Learn From Mistakes on Grade-School Math Problems"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2408.16293","kind":"arxiv","version":1},"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:b633885acdb833ce11d511d86f0d3dbfd5baf1a7c4e3c0390d842b0e32d3f46f","target":"record","created_at":"2026-07-05T09:00:28Z","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":"c4638d47942c89e3f8858d0115140cfe621e38f0b6b4b2c993c77b4016ebb87b","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-08-29T06:49:20Z","title_canon_sha256":"3065d82db88c779a0a3fab9e52304d6d312e8a38bf699a79327d62d606380dcc"},"schema_version":"1.0","source":{"id":"2408.16293","kind":"arxiv","version":1}},"canonical_sha256":"f72f8a5def59b7ea44a1ac7de2dd9dd6d3d59e5a0ba7d83874ab6a915f93768f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f72f8a5def59b7ea44a1ac7de2dd9dd6d3d59e5a0ba7d83874ab6a915f93768f","first_computed_at":"2026-07-05T09:00:28.977720Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:00:28.977720Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"xxBWGvQiSaPFhttHGSeXbZvBCi4aWXRyRwU20NeQ5+JmdSw58vNAZDyt690Xksd11XpGeL7Kwvuyl/kzz8euBQ==","signature_status":"signed_v1","signed_at":"2026-07-05T09:00:28.978164Z","signed_message":"canonical_sha256_bytes"},"source_id":"2408.16293","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b633885acdb833ce11d511d86f0d3dbfd5baf1a7c4e3c0390d842b0e32d3f46f","sha256:0718f2b5659e9954dbd2f5cd0cff412b2bcea3380b0e39ab32160fb4a288e25f"],"state_sha256":"390de2bf4674abad299d48201392231e10ca1a9f221b097ca9dd6bc59840c78e"}