{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:OK54ROZBZQBJTRGI2IVLP46OYR","short_pith_number":"pith:OK54ROZB","schema_version":"1.0","canonical_sha256":"72bbc8bb21cc0299c4c8d22ab7f3cec4486ebc5ab73d04d2c73d7f03e3d1feb4","source":{"kind":"arxiv","id":"2307.10936","version":2},"attestation_state":"computed","paper":{"title":"PASTA: Pretrained Action-State Transformer Agents","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.AI","authors_text":"Arthur Flajolet, Guillaume Richard, Raphael Boige, Thomas Pierrot, Yannis Flet-Berliac","submitted_at":"2023-07-20T15:09:06Z","abstract_excerpt":"Self-supervised learning has brought about a revolutionary paradigm shift in various computing domains, including NLP, vision, and biology. Recent approaches involve pre-training transformer models on vast amounts of unlabeled data, serving as a starting point for efficiently solving downstream tasks. In reinforcement learning, researchers have recently adapted these approaches, developing models pre-trained on expert trajectories. This advancement enables the models to tackle a broad spectrum of tasks, ranging from robotics to recommendation systems. However, existing methods mostly rely on i"},"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":"2307.10936","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2023-07-20T15:09:06Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"cde3012a7248188db276e13c19765f205a6fc05b3ecce6148a43555c84a0bcee","abstract_canon_sha256":"723846dd14833e5243c1a32721625307f3d5b8036c0f8b244726112562bc5ea5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:19:47.437002Z","signature_b64":"wTEOkoV6gnkdNI098AQjJ6Em+vb2lR7tC3XPr/Pf7Z2UZdIsnOK3LMMAXje06zQZv9caQp+mmgVaX0Yr1fpxBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"72bbc8bb21cc0299c4c8d22ab7f3cec4486ebc5ab73d04d2c73d7f03e3d1feb4","last_reissued_at":"2026-07-05T07:19:47.436517Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:19:47.436517Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"PASTA: Pretrained Action-State Transformer Agents","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.AI","authors_text":"Arthur Flajolet, Guillaume Richard, Raphael Boige, Thomas Pierrot, Yannis Flet-Berliac","submitted_at":"2023-07-20T15:09:06Z","abstract_excerpt":"Self-supervised learning has brought about a revolutionary paradigm shift in various computing domains, including NLP, vision, and biology. Recent approaches involve pre-training transformer models on vast amounts of unlabeled data, serving as a starting point for efficiently solving downstream tasks. In reinforcement learning, researchers have recently adapted these approaches, developing models pre-trained on expert trajectories. This advancement enables the models to tackle a broad spectrum of tasks, ranging from robotics to recommendation systems. However, existing methods mostly rely on i"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2307.10936","kind":"arxiv","version":2},"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/2307.10936/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":"2307.10936","created_at":"2026-07-05T07:19:47.436571+00:00"},{"alias_kind":"arxiv_version","alias_value":"2307.10936v2","created_at":"2026-07-05T07:19:47.436571+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2307.10936","created_at":"2026-07-05T07:19:47.436571+00:00"},{"alias_kind":"pith_short_12","alias_value":"OK54ROZBZQBJ","created_at":"2026-07-05T07:19:47.436571+00:00"},{"alias_kind":"pith_short_16","alias_value":"OK54ROZBZQBJTRGI","created_at":"2026-07-05T07:19:47.436571+00:00"},{"alias_kind":"pith_short_8","alias_value":"OK54ROZB","created_at":"2026-07-05T07:19:47.436571+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/OK54ROZBZQBJTRGI2IVLP46OYR","json":"https://pith.science/pith/OK54ROZBZQBJTRGI2IVLP46OYR.json","graph_json":"https://pith.science/api/pith-number/OK54ROZBZQBJTRGI2IVLP46OYR/graph.json","events_json":"https://pith.science/api/pith-number/OK54ROZBZQBJTRGI2IVLP46OYR/events.json","paper":"https://pith.science/paper/OK54ROZB"},"agent_actions":{"view_html":"https://pith.science/pith/OK54ROZBZQBJTRGI2IVLP46OYR","download_json":"https://pith.science/pith/OK54ROZBZQBJTRGI2IVLP46OYR.json","view_paper":"https://pith.science/paper/OK54ROZB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2307.10936&json=true","fetch_graph":"https://pith.science/api/pith-number/OK54ROZBZQBJTRGI2IVLP46OYR/graph.json","fetch_events":"https://pith.science/api/pith-number/OK54ROZBZQBJTRGI2IVLP46OYR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OK54ROZBZQBJTRGI2IVLP46OYR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OK54ROZBZQBJTRGI2IVLP46OYR/action/storage_attestation","attest_author":"https://pith.science/pith/OK54ROZBZQBJTRGI2IVLP46OYR/action/author_attestation","sign_citation":"https://pith.science/pith/OK54ROZBZQBJTRGI2IVLP46OYR/action/citation_signature","submit_replication":"https://pith.science/pith/OK54ROZBZQBJTRGI2IVLP46OYR/action/replication_record"}},"created_at":"2026-07-05T07:19:47.436571+00:00","updated_at":"2026-07-05T07:19:47.436571+00:00"}