{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:X4SSEHLL5CPOXDZQLIC2QOQ27H","short_pith_number":"pith:X4SSEHLL","canonical_record":{"source":{"id":"2411.17900","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"q-fin.CP","submitted_at":"2024-11-26T21:31:58Z","cross_cats_sorted":[],"title_canon_sha256":"fbd40777d1116343de2774d405044334daf16b2373b4f7074ec441ab54339903","abstract_canon_sha256":"2c9bcb9c7f8bca03a99be51e694b9a9d6fa7ef164e901bc3392db676e4a1eadd"},"schema_version":"1.0"},"canonical_sha256":"bf25221d6be89eeb8f305a05a83a1af9f4f84ea54537af5a2d7778c3b3067b91","source":{"kind":"arxiv","id":"2411.17900","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2411.17900","created_at":"2026-07-05T09:41:08Z"},{"alias_kind":"arxiv_version","alias_value":"2411.17900v1","created_at":"2026-07-05T09:41:08Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.17900","created_at":"2026-07-05T09:41:08Z"},{"alias_kind":"pith_short_12","alias_value":"X4SSEHLL5CPO","created_at":"2026-07-05T09:41:08Z"},{"alias_kind":"pith_short_16","alias_value":"X4SSEHLL5CPOXDZQ","created_at":"2026-07-05T09:41:08Z"},{"alias_kind":"pith_short_8","alias_value":"X4SSEHLL","created_at":"2026-07-05T09:41:08Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:X4SSEHLL5CPOXDZQLIC2QOQ27H","target":"record","payload":{"canonical_record":{"source":{"id":"2411.17900","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"q-fin.CP","submitted_at":"2024-11-26T21:31:58Z","cross_cats_sorted":[],"title_canon_sha256":"fbd40777d1116343de2774d405044334daf16b2373b4f7074ec441ab54339903","abstract_canon_sha256":"2c9bcb9c7f8bca03a99be51e694b9a9d6fa7ef164e901bc3392db676e4a1eadd"},"schema_version":"1.0"},"canonical_sha256":"bf25221d6be89eeb8f305a05a83a1af9f4f84ea54537af5a2d7778c3b3067b91","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:41:08.826787Z","signature_b64":"J/oxVhWHvptEqw4FRXBGRuCYBsak2bc7DZMR0pSF7yg68TTQ7tHQIDagcX3TnTBEk+jroHwhtFnQ0CViyN0UAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bf25221d6be89eeb8f305a05a83a1af9f4f84ea54537af5a2d7778c3b3067b91","last_reissued_at":"2026-07-05T09:41:08.826305Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:41:08.826305Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2411.17900","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T09:41:08Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"yAzYDByWIcmiYsGOIs41C98dA7Icv+TZ3mXJk5iTV8Gei6n6iXNkcgQRtSs1eh4sIPlhOD6Tq/+GR5DiU2oFDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-13T04:23:05.893964Z"},"content_sha256":"46d1300cb8a1d0c5683deac02becfd85f601bfc26cb5f846aba2fab91ba60aed","schema_version":"1.0","event_id":"sha256:46d1300cb8a1d0c5683deac02becfd85f601bfc26cb5f846aba2fab91ba60aed"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:X4SSEHLL5CPOXDZQLIC2QOQ27H","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Pretrained LLM Adapted with LoRA as a Decision Transformer for Offline RL in Quantitative Trading","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"q-fin.CP","authors_text":"Suyeol Yun","submitted_at":"2024-11-26T21:31:58Z","abstract_excerpt":"Developing effective quantitative trading strategies using reinforcement learning (RL) is challenging due to the high risks associated with online interaction with live financial markets. Consequently, offline RL, which leverages historical market data without additional exploration, becomes essential. However, existing offline RL methods often struggle to capture the complex temporal dependencies inherent in financial time series and may overfit to historical patterns. To address these challenges, we introduce a Decision Transformer (DT) initialized with pre-trained GPT-2 weights and fine-tun"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.17900","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/2411.17900/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T09:41:08Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"1PaN1KjNbwvFjV7vir1Rv6f/eyOfWvQsY6TtlGI51wsyOUTVZmPFq57rvnoYHOy2IgYhRrbHAS4Se2TlgS0ICA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-13T04:23:05.894332Z"},"content_sha256":"c6a612108f2b634bc41619eaffeee6d690a890518b3e9503e8926a26d3e0115f","schema_version":"1.0","event_id":"sha256:c6a612108f2b634bc41619eaffeee6d690a890518b3e9503e8926a26d3e0115f"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/X4SSEHLL5CPOXDZQLIC2QOQ27H/bundle.json","state_url":"https://pith.science/pith/X4SSEHLL5CPOXDZQLIC2QOQ27H/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/X4SSEHLL5CPOXDZQLIC2QOQ27H/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-13T04:23:05Z","links":{"resolver":"https://pith.science/pith/X4SSEHLL5CPOXDZQLIC2QOQ27H","bundle":"https://pith.science/pith/X4SSEHLL5CPOXDZQLIC2QOQ27H/bundle.json","state":"https://pith.science/pith/X4SSEHLL5CPOXDZQLIC2QOQ27H/state.json","well_known_bundle":"https://pith.science/.well-known/pith/X4SSEHLL5CPOXDZQLIC2QOQ27H/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:X4SSEHLL5CPOXDZQLIC2QOQ27H","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":"2c9bcb9c7f8bca03a99be51e694b9a9d6fa7ef164e901bc3392db676e4a1eadd","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"q-fin.CP","submitted_at":"2024-11-26T21:31:58Z","title_canon_sha256":"fbd40777d1116343de2774d405044334daf16b2373b4f7074ec441ab54339903"},"schema_version":"1.0","source":{"id":"2411.17900","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2411.17900","created_at":"2026-07-05T09:41:08Z"},{"alias_kind":"arxiv_version","alias_value":"2411.17900v1","created_at":"2026-07-05T09:41:08Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.17900","created_at":"2026-07-05T09:41:08Z"},{"alias_kind":"pith_short_12","alias_value":"X4SSEHLL5CPO","created_at":"2026-07-05T09:41:08Z"},{"alias_kind":"pith_short_16","alias_value":"X4SSEHLL5CPOXDZQ","created_at":"2026-07-05T09:41:08Z"},{"alias_kind":"pith_short_8","alias_value":"X4SSEHLL","created_at":"2026-07-05T09:41:08Z"}],"graph_snapshots":[{"event_id":"sha256:c6a612108f2b634bc41619eaffeee6d690a890518b3e9503e8926a26d3e0115f","target":"graph","created_at":"2026-07-05T09:41:08Z","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/2411.17900/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Developing effective quantitative trading strategies using reinforcement learning (RL) is challenging due to the high risks associated with online interaction with live financial markets. Consequently, offline RL, which leverages historical market data without additional exploration, becomes essential. However, existing offline RL methods often struggle to capture the complex temporal dependencies inherent in financial time series and may overfit to historical patterns. To address these challenges, we introduce a Decision Transformer (DT) initialized with pre-trained GPT-2 weights and fine-tun","authors_text":"Suyeol Yun","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"q-fin.CP","submitted_at":"2024-11-26T21:31:58Z","title":"Pretrained LLM Adapted with LoRA as a Decision Transformer for Offline RL in Quantitative Trading"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.17900","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":"sha256:46d1300cb8a1d0c5683deac02becfd85f601bfc26cb5f846aba2fab91ba60aed","target":"record","created_at":"2026-07-05T09:41:08Z","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":"2c9bcb9c7f8bca03a99be51e694b9a9d6fa7ef164e901bc3392db676e4a1eadd","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"q-fin.CP","submitted_at":"2024-11-26T21:31:58Z","title_canon_sha256":"fbd40777d1116343de2774d405044334daf16b2373b4f7074ec441ab54339903"},"schema_version":"1.0","source":{"id":"2411.17900","kind":"arxiv","version":1}},"canonical_sha256":"bf25221d6be89eeb8f305a05a83a1af9f4f84ea54537af5a2d7778c3b3067b91","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"bf25221d6be89eeb8f305a05a83a1af9f4f84ea54537af5a2d7778c3b3067b91","first_computed_at":"2026-07-05T09:41:08.826305Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:41:08.826305Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"J/oxVhWHvptEqw4FRXBGRuCYBsak2bc7DZMR0pSF7yg68TTQ7tHQIDagcX3TnTBEk+jroHwhtFnQ0CViyN0UAA==","signature_status":"signed_v1","signed_at":"2026-07-05T09:41:08.826787Z","signed_message":"canonical_sha256_bytes"},"source_id":"2411.17900","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:46d1300cb8a1d0c5683deac02becfd85f601bfc26cb5f846aba2fab91ba60aed","sha256:c6a612108f2b634bc41619eaffeee6d690a890518b3e9503e8926a26d3e0115f"],"state_sha256":"fb18513ef48c62123267cd944c14915a7f5a65e63ba2e51d0bc4590ba7985986"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"z8eaGbcwPT7kV6XSpadBP6La1M/IxZpVoFWUMt+8OGOUnzvSTld26MvAiVSwFk24spH/TvhGXRQFN+LCbBK8BQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-13T04:23:05.896786Z","bundle_sha256":"1e7f53310ba0e6aeef1772347e35ca50a07fe7f9e29eb4267f023442b5b8b1a5"}}