{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:HQSDMYTU2HN5LZUNPAVLVW72YV","short_pith_number":"pith:HQSDMYTU","canonical_record":{"source":{"id":"2305.20081","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2023-05-31T17:55:21Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"817776701ac7f6852343c99861c55921f70b742b8adc85321148dfd06ef7d207","abstract_canon_sha256":"2c5c761972742fb38ed3e76936409a99f9bf8355787bb37a42db31989fc65dcf"},"schema_version":"1.0"},"canonical_sha256":"3c24366274d1dbd5e68d782abadbfac574968cd3066aa3270acd6a6a01a71343","source":{"kind":"arxiv","id":"2305.20081","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2305.20081","created_at":"2026-07-05T07:05:31Z"},{"alias_kind":"arxiv_version","alias_value":"2305.20081v2","created_at":"2026-07-05T07:05:31Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.20081","created_at":"2026-07-05T07:05:31Z"},{"alias_kind":"pith_short_12","alias_value":"HQSDMYTU2HN5","created_at":"2026-07-05T07:05:31Z"},{"alias_kind":"pith_short_16","alias_value":"HQSDMYTU2HN5LZUN","created_at":"2026-07-05T07:05:31Z"},{"alias_kind":"pith_short_8","alias_value":"HQSDMYTU","created_at":"2026-07-05T07:05:31Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:HQSDMYTU2HN5LZUNPAVLVW72YV","target":"record","payload":{"canonical_record":{"source":{"id":"2305.20081","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2023-05-31T17:55:21Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"817776701ac7f6852343c99861c55921f70b742b8adc85321148dfd06ef7d207","abstract_canon_sha256":"2c5c761972742fb38ed3e76936409a99f9bf8355787bb37a42db31989fc65dcf"},"schema_version":"1.0"},"canonical_sha256":"3c24366274d1dbd5e68d782abadbfac574968cd3066aa3270acd6a6a01a71343","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:05:31.213291Z","signature_b64":"jlB90M3vtIFhnlrOrDsqIl/yCr0j7rNgH/C00NRrfMa/UG7QYhpnVs91Co4i66J7+RF7BDH8DXlA/Dh4HL0tAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3c24366274d1dbd5e68d782abadbfac574968cd3066aa3270acd6a6a01a71343","last_reissued_at":"2026-07-05T07:05:31.212849Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:05:31.212849Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2305.20081","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T07:05:31Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"hDFnq7Qg4iY9Njdl7KbNskMxxngJMiqe2yQe1cG+QlO9M7OVTcCXl14vauQ31EbmJRNmLb9T8pzi6IpXeo9ZDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T13:15:35.047010Z"},"content_sha256":"2c8478eaf0a85e8e62d16e850f83a343d999f7356221ddc05c4056d515aff717","schema_version":"1.0","event_id":"sha256:2c8478eaf0a85e8e62d16e850f83a343d999f7356221ddc05c4056d515aff717"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:HQSDMYTU2HN5LZUNPAVLVW72YV","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Efficient Diffusion Policies for Offline Reinforcement Learning","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Bingyi Kang, Chao Du, Shuicheng Yan, Tianyu Pang, Xiao Ma","submitted_at":"2023-05-31T17:55:21Z","abstract_excerpt":"Offline reinforcement learning (RL) aims to learn optimal policies from offline datasets, where the parameterization of policies is crucial but often overlooked. Recently, Diffsuion-QL significantly boosts the performance of offline RL by representing a policy with a diffusion model, whose success relies on a parametrized Markov Chain with hundreds of steps for sampling. However, Diffusion-QL suffers from two critical limitations. 1) It is computationally inefficient to forward and backward through the whole Markov chain during training. 2) It is incompatible with maximum likelihood-based RL a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.20081","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/2305.20081/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":"2026-07-05T07:05:31Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"GV/FnRlQDq5T/+KMjvhB72uK9EIqyWutiNqRLKf6+ehE9ClfO6Xn9aPdXKXwtrPTfgmpa0eRpdH9ribVsQRoAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T13:15:35.047606Z"},"content_sha256":"88546956b83712c95cb61df3f0e47def6518bf3153fc476ac10202df852b0109","schema_version":"1.0","event_id":"sha256:88546956b83712c95cb61df3f0e47def6518bf3153fc476ac10202df852b0109"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/HQSDMYTU2HN5LZUNPAVLVW72YV/bundle.json","state_url":"https://pith.science/pith/HQSDMYTU2HN5LZUNPAVLVW72YV/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/HQSDMYTU2HN5LZUNPAVLVW72YV/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":"2026-08-08T13:15:35Z","links":{"resolver":"https://pith.science/pith/HQSDMYTU2HN5LZUNPAVLVW72YV","bundle":"https://pith.science/pith/HQSDMYTU2HN5LZUNPAVLVW72YV/bundle.json","state":"https://pith.science/pith/HQSDMYTU2HN5LZUNPAVLVW72YV/state.json","well_known_bundle":"https://pith.science/.well-known/pith/HQSDMYTU2HN5LZUNPAVLVW72YV/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:HQSDMYTU2HN5LZUNPAVLVW72YV","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":"2c5c761972742fb38ed3e76936409a99f9bf8355787bb37a42db31989fc65dcf","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2023-05-31T17:55:21Z","title_canon_sha256":"817776701ac7f6852343c99861c55921f70b742b8adc85321148dfd06ef7d207"},"schema_version":"1.0","source":{"id":"2305.20081","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2305.20081","created_at":"2026-07-05T07:05:31Z"},{"alias_kind":"arxiv_version","alias_value":"2305.20081v2","created_at":"2026-07-05T07:05:31Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.20081","created_at":"2026-07-05T07:05:31Z"},{"alias_kind":"pith_short_12","alias_value":"HQSDMYTU2HN5","created_at":"2026-07-05T07:05:31Z"},{"alias_kind":"pith_short_16","alias_value":"HQSDMYTU2HN5LZUN","created_at":"2026-07-05T07:05:31Z"},{"alias_kind":"pith_short_8","alias_value":"HQSDMYTU","created_at":"2026-07-05T07:05:31Z"}],"graph_snapshots":[{"event_id":"sha256:88546956b83712c95cb61df3f0e47def6518bf3153fc476ac10202df852b0109","target":"graph","created_at":"2026-07-05T07:05:31Z","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/2305.20081/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Offline reinforcement learning (RL) aims to learn optimal policies from offline datasets, where the parameterization of policies is crucial but often overlooked. Recently, Diffsuion-QL significantly boosts the performance of offline RL by representing a policy with a diffusion model, whose success relies on a parametrized Markov Chain with hundreds of steps for sampling. However, Diffusion-QL suffers from two critical limitations. 1) It is computationally inefficient to forward and backward through the whole Markov chain during training. 2) It is incompatible with maximum likelihood-based RL a","authors_text":"Bingyi Kang, Chao Du, Shuicheng Yan, Tianyu Pang, Xiao Ma","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2023-05-31T17:55:21Z","title":"Efficient Diffusion Policies for Offline Reinforcement Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.20081","kind":"arxiv","version":2},"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":"sha256:2c8478eaf0a85e8e62d16e850f83a343d999f7356221ddc05c4056d515aff717","target":"record","created_at":"2026-07-05T07:05:31Z","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":"2c5c761972742fb38ed3e76936409a99f9bf8355787bb37a42db31989fc65dcf","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2023-05-31T17:55:21Z","title_canon_sha256":"817776701ac7f6852343c99861c55921f70b742b8adc85321148dfd06ef7d207"},"schema_version":"1.0","source":{"id":"2305.20081","kind":"arxiv","version":2}},"canonical_sha256":"3c24366274d1dbd5e68d782abadbfac574968cd3066aa3270acd6a6a01a71343","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"3c24366274d1dbd5e68d782abadbfac574968cd3066aa3270acd6a6a01a71343","first_computed_at":"2026-07-05T07:05:31.212849Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:05:31.212849Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"jlB90M3vtIFhnlrOrDsqIl/yCr0j7rNgH/C00NRrfMa/UG7QYhpnVs91Co4i66J7+RF7BDH8DXlA/Dh4HL0tAg==","signature_status":"signed_v1","signed_at":"2026-07-05T07:05:31.213291Z","signed_message":"canonical_sha256_bytes"},"source_id":"2305.20081","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:2c8478eaf0a85e8e62d16e850f83a343d999f7356221ddc05c4056d515aff717","sha256:88546956b83712c95cb61df3f0e47def6518bf3153fc476ac10202df852b0109"],"state_sha256":"56490b4c9fdf14da78d3e007447fd0b2906280c6cea1fc1649f01ff93b67f23f"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"UBLRtNaHjvU6K8al0z6shYrWUBUd6iPrBjS69YWBRo7ydxtOEaZr3uFt3nD1u1ucKxFH5tPCV7zVfYKoFaoxDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-08T13:15:35.059439Z","bundle_sha256":"f5b35a4dbc5917846ff871fe0b79a5f7a3335aa4a2f4d16a55575df6eef3849b"}}