{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:BZGJMBWNVUF4AFHWAGRQ22MREC","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":"bffc2d1885faa30b65d41c5e8b3a16e4e2b50241916ecd34db3a76af4bfb6cd2","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2024-02-05T17:30:42Z","title_canon_sha256":"ba2a1b2b6e52c0696fcca72aadccf1f313b34e7189ca452091a77ba532205e2f"},"schema_version":"1.0","source":{"id":"2402.03220","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2402.03220","created_at":"2026-07-05T09:03:18Z"},{"alias_kind":"arxiv_version","alias_value":"2402.03220v3","created_at":"2026-07-05T09:03:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.03220","created_at":"2026-07-05T09:03:18Z"},{"alias_kind":"pith_short_12","alias_value":"BZGJMBWNVUF4","created_at":"2026-07-05T09:03:18Z"},{"alias_kind":"pith_short_16","alias_value":"BZGJMBWNVUF4AFHW","created_at":"2026-07-05T09:03:18Z"},{"alias_kind":"pith_short_8","alias_value":"BZGJMBWN","created_at":"2026-07-05T09:03:18Z"}],"graph_snapshots":[{"event_id":"sha256:e2daf1bcd30a4558795afe9b65d1365b0120391826f8be4c8a28147a4dffe4d8","target":"graph","created_at":"2026-07-05T09:03:18Z","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/2402.03220/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We investigate the training dynamics of two-layer neural networks when learning multi-index target functions. We focus on multi-pass gradient descent (GD) that reuses the batches multiple times and show that it significantly changes the conclusion about which functions are learnable compared to single-pass gradient descent. In particular, multi-pass GD with finite stepsize is found to overcome the limitations of gradient flow and single-pass GD given by the information exponent (Ben Arous et al., 2021) and leap exponent (Abbe et al., 2023) of the target function. We show that upon re-using bat","authors_text":"Emanuele Troiani, Florent Krzakala, Lenka Zdeborov\\'a, Luca Arnaboldi, Luca Pesce, Yatin Dandi","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2024-02-05T17:30:42Z","title":"The Benefits of Reusing Batches for Gradient Descent in Two-Layer Networks: Breaking the Curse of Information and Leap Exponents"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.03220","kind":"arxiv","version":3},"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":"sha256:8ffe11baf3fab20f25656109ed1041b3e12c862fb6b8e9ecdb59dad2fce7b7a5","target":"record","created_at":"2026-07-05T09:03:18Z","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":"bffc2d1885faa30b65d41c5e8b3a16e4e2b50241916ecd34db3a76af4bfb6cd2","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2024-02-05T17:30:42Z","title_canon_sha256":"ba2a1b2b6e52c0696fcca72aadccf1f313b34e7189ca452091a77ba532205e2f"},"schema_version":"1.0","source":{"id":"2402.03220","kind":"arxiv","version":3}},"canonical_sha256":"0e4c9606cdad0bc014f601a30d69912089da77c72ab91c4518f651c545edcc02","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"0e4c9606cdad0bc014f601a30d69912089da77c72ab91c4518f651c545edcc02","first_computed_at":"2026-07-05T09:03:18.305580Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:03:18.305580Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"81JyjQIUisi7Xa+bqY6PCu0o+ItpNn3LABy2sx23fr53EvHwEHdMgyTihxY/aAJZwBUAu7kBbuMx7/Eq9Zq1CQ==","signature_status":"signed_v1","signed_at":"2026-07-05T09:03:18.306151Z","signed_message":"canonical_sha256_bytes"},"source_id":"2402.03220","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:8ffe11baf3fab20f25656109ed1041b3e12c862fb6b8e9ecdb59dad2fce7b7a5","sha256:e2daf1bcd30a4558795afe9b65d1365b0120391826f8be4c8a28147a4dffe4d8"],"state_sha256":"367ed992493465bf7acc58edae0766e84513ec52fe15af0d5f2eb807d7ebfd0c"}