{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:TXR7FE6X47GSCRG55IMHPEN2SV","short_pith_number":"pith:TXR7FE6X","schema_version":"1.0","canonical_sha256":"9de3f293d7e7cd2144ddea187791ba95409eb2a59377615f3eb257791331da68","source":{"kind":"arxiv","id":"2111.05188","version":1},"attestation_state":"computed","paper":{"title":"FinRL-Podracer: High Performance and Scalable Deep Reinforcement Learning for Quantitative Finance","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CE"],"primary_cat":"q-fin.CP","authors_text":"Anwar Walid, Jiahao Zheng, Jian Guo, Xiao-Yang Liu, Zechu Li, Zhaoran Wang","submitted_at":"2021-11-07T01:03:15Z","abstract_excerpt":"Machine learning techniques are playing more and more important roles in finance market investment. However, finance quantitative modeling with conventional supervised learning approaches has a number of limitations. The development of deep reinforcement learning techniques is partially addressing these issues. Unfortunately, the steep learning curve and the difficulty in quick modeling and agile development are impeding finance researchers from using deep reinforcement learning in quantitative trading. In this paper, we propose an RLOps in finance paradigm and present a FinRL-Podracer framewo"},"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":"2111.05188","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"q-fin.CP","submitted_at":"2021-11-07T01:03:15Z","cross_cats_sorted":["cs.CE"],"title_canon_sha256":"d7a729489432879456ec6fa69d014ddd4c31b8c87f584d80d2c00392bf154ca9","abstract_canon_sha256":"8c704f784e95d53452e90d695fe6ba020e61b3380da829b6d1a674da1af248da"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:30:22.398049Z","signature_b64":"NRipUFue7yq/qDqE8pnXBk/lqSxrBzH7U3k/ksCPxrJIDa2Vxxg6hfXz/YYNoVx0xzIjFw+wM2UTEz+wVXwQAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9de3f293d7e7cd2144ddea187791ba95409eb2a59377615f3eb257791331da68","last_reissued_at":"2026-07-05T03:30:22.397592Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:30:22.397592Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"FinRL-Podracer: High Performance and Scalable Deep Reinforcement Learning for Quantitative Finance","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CE"],"primary_cat":"q-fin.CP","authors_text":"Anwar Walid, Jiahao Zheng, Jian Guo, Xiao-Yang Liu, Zechu Li, Zhaoran Wang","submitted_at":"2021-11-07T01:03:15Z","abstract_excerpt":"Machine learning techniques are playing more and more important roles in finance market investment. However, finance quantitative modeling with conventional supervised learning approaches has a number of limitations. The development of deep reinforcement learning techniques is partially addressing these issues. Unfortunately, the steep learning curve and the difficulty in quick modeling and agile development are impeding finance researchers from using deep reinforcement learning in quantitative trading. In this paper, we propose an RLOps in finance paradigm and present a FinRL-Podracer framewo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2111.05188","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/2111.05188/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":"2111.05188","created_at":"2026-07-05T03:30:22.397650+00:00"},{"alias_kind":"arxiv_version","alias_value":"2111.05188v1","created_at":"2026-07-05T03:30:22.397650+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2111.05188","created_at":"2026-07-05T03:30:22.397650+00:00"},{"alias_kind":"pith_short_12","alias_value":"TXR7FE6X47GS","created_at":"2026-07-05T03:30:22.397650+00:00"},{"alias_kind":"pith_short_16","alias_value":"TXR7FE6X47GSCRG5","created_at":"2026-07-05T03:30:22.397650+00:00"},{"alias_kind":"pith_short_8","alias_value":"TXR7FE6X","created_at":"2026-07-05T03:30:22.397650+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2502.17011","citing_title":"Predicting Liquidity-Aware Bond Yields using Causal GANs and Deep Reinforcement Learning with LLM Evaluation","ref_index":18,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/TXR7FE6X47GSCRG55IMHPEN2SV","json":"https://pith.science/pith/TXR7FE6X47GSCRG55IMHPEN2SV.json","graph_json":"https://pith.science/api/pith-number/TXR7FE6X47GSCRG55IMHPEN2SV/graph.json","events_json":"https://pith.science/api/pith-number/TXR7FE6X47GSCRG55IMHPEN2SV/events.json","paper":"https://pith.science/paper/TXR7FE6X"},"agent_actions":{"view_html":"https://pith.science/pith/TXR7FE6X47GSCRG55IMHPEN2SV","download_json":"https://pith.science/pith/TXR7FE6X47GSCRG55IMHPEN2SV.json","view_paper":"https://pith.science/paper/TXR7FE6X","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2111.05188&json=true","fetch_graph":"https://pith.science/api/pith-number/TXR7FE6X47GSCRG55IMHPEN2SV/graph.json","fetch_events":"https://pith.science/api/pith-number/TXR7FE6X47GSCRG55IMHPEN2SV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TXR7FE6X47GSCRG55IMHPEN2SV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TXR7FE6X47GSCRG55IMHPEN2SV/action/storage_attestation","attest_author":"https://pith.science/pith/TXR7FE6X47GSCRG55IMHPEN2SV/action/author_attestation","sign_citation":"https://pith.science/pith/TXR7FE6X47GSCRG55IMHPEN2SV/action/citation_signature","submit_replication":"https://pith.science/pith/TXR7FE6X47GSCRG55IMHPEN2SV/action/replication_record"}},"created_at":"2026-07-05T03:30:22.397650+00:00","updated_at":"2026-07-05T03:30:22.397650+00:00"}