{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:DE3NRR5SNKTKT73NTTBNRVQJG7","short_pith_number":"pith:DE3NRR5S","canonical_record":{"source":{"id":"2301.13573","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-01-31T11:52:46Z","cross_cats_sorted":[],"title_canon_sha256":"f802cab849f56e0edd4440a2851e8426d33ae936a9e6c9cf13c4614479212bfe","abstract_canon_sha256":"61b7896ca16eab302301c728b8d426b5b6c7545279ac4752f9e29111ca2efe14"},"schema_version":"1.0"},"canonical_sha256":"1936d8c7b26aa6a9ff6d9cc2d8d60937e835862226e7443d97f4d59e4dcc0d0b","source":{"kind":"arxiv","id":"2301.13573","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2301.13573","created_at":"2026-07-05T05:37:16Z"},{"alias_kind":"arxiv_version","alias_value":"2301.13573v1","created_at":"2026-07-05T05:37:16Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2301.13573","created_at":"2026-07-05T05:37:16Z"},{"alias_kind":"pith_short_12","alias_value":"DE3NRR5SNKTK","created_at":"2026-07-05T05:37:16Z"},{"alias_kind":"pith_short_16","alias_value":"DE3NRR5SNKTKT73N","created_at":"2026-07-05T05:37:16Z"},{"alias_kind":"pith_short_8","alias_value":"DE3NRR5S","created_at":"2026-07-05T05:37:16Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:DE3NRR5SNKTKT73NTTBNRVQJG7","target":"record","payload":{"canonical_record":{"source":{"id":"2301.13573","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-01-31T11:52:46Z","cross_cats_sorted":[],"title_canon_sha256":"f802cab849f56e0edd4440a2851e8426d33ae936a9e6c9cf13c4614479212bfe","abstract_canon_sha256":"61b7896ca16eab302301c728b8d426b5b6c7545279ac4752f9e29111ca2efe14"},"schema_version":"1.0"},"canonical_sha256":"1936d8c7b26aa6a9ff6d9cc2d8d60937e835862226e7443d97f4d59e4dcc0d0b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:37:16.277478Z","signature_b64":"4BRcEtYDqW+P6HVOyrQ5fZKWuDwP3wcuW2KD7Xwwh9FyhDD4SZoLIiiq87d5XfaKPfKeahyvDBLxBvFDGSHKAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1936d8c7b26aa6a9ff6d9cc2d8d60937e835862226e7443d97f4d59e4dcc0d0b","last_reissued_at":"2026-07-05T05:37:16.276964Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:37:16.276964Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2301.13573","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":"2026-07-05T05:37:16Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"A/s8RBFdqXMF9vUz8UhZEaskcA9WAgV79CCvi/XTJVS8gOvOSZ/2fK5XFEkV2WIgypj9QNK3hZvtA/IvNeS4CA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-18T16:10:35.753716Z"},"content_sha256":"1b3e2d57ae8c1a3b7d7463b49a551233b6c795e66f2ec135070ac468f655caef","schema_version":"1.0","event_id":"sha256:1b3e2d57ae8c1a3b7d7463b49a551233b6c795e66f2ec135070ac468f655caef"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:DE3NRR5SNKTKT73NTTBNRVQJG7","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Skill Decision Transformer","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Sebastian Risi, Shyam Sudhakaran","submitted_at":"2023-01-31T11:52:46Z","abstract_excerpt":"Recent work has shown that Large Language Models (LLMs) can be incredibly effective for offline reinforcement learning (RL) by representing the traditional RL problem as a sequence modelling problem (Chen et al., 2021; Janner et al., 2021). However many of these methods only optimize for high returns, and may not extract much information from a diverse dataset of trajectories. Generalized Decision Transformers (GDTs) (Furuta et al., 2021) have shown that utilizing future trajectory information, in the form of information statistics, can help extract more information from offline trajectory dat"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2301.13573","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/2301.13573/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":"2026-07-05T05:37:16Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Ye3JakGUYd2X2OpSoaWdg1JCSiX5k7HpMeE7JHTk/dZZ/y5cHfD04MGxb2UBZmb2VLiZunBZzW4p2jZHrEcKDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-18T16:10:35.754408Z"},"content_sha256":"70f3627036b1ad565186036901f49c6d2a73eb7dfd741f516906abb201e139db","schema_version":"1.0","event_id":"sha256:70f3627036b1ad565186036901f49c6d2a73eb7dfd741f516906abb201e139db"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/DE3NRR5SNKTKT73NTTBNRVQJG7/bundle.json","state_url":"https://pith.science/pith/DE3NRR5SNKTKT73NTTBNRVQJG7/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/DE3NRR5SNKTKT73NTTBNRVQJG7/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":"2026-08-18T16:10:35Z","links":{"resolver":"https://pith.science/pith/DE3NRR5SNKTKT73NTTBNRVQJG7","bundle":"https://pith.science/pith/DE3NRR5SNKTKT73NTTBNRVQJG7/bundle.json","state":"https://pith.science/pith/DE3NRR5SNKTKT73NTTBNRVQJG7/state.json","well_known_bundle":"https://pith.science/.well-known/pith/DE3NRR5SNKTKT73NTTBNRVQJG7/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:DE3NRR5SNKTKT73NTTBNRVQJG7","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":"61b7896ca16eab302301c728b8d426b5b6c7545279ac4752f9e29111ca2efe14","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-01-31T11:52:46Z","title_canon_sha256":"f802cab849f56e0edd4440a2851e8426d33ae936a9e6c9cf13c4614479212bfe"},"schema_version":"1.0","source":{"id":"2301.13573","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2301.13573","created_at":"2026-07-05T05:37:16Z"},{"alias_kind":"arxiv_version","alias_value":"2301.13573v1","created_at":"2026-07-05T05:37:16Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2301.13573","created_at":"2026-07-05T05:37:16Z"},{"alias_kind":"pith_short_12","alias_value":"DE3NRR5SNKTK","created_at":"2026-07-05T05:37:16Z"},{"alias_kind":"pith_short_16","alias_value":"DE3NRR5SNKTKT73N","created_at":"2026-07-05T05:37:16Z"},{"alias_kind":"pith_short_8","alias_value":"DE3NRR5S","created_at":"2026-07-05T05:37:16Z"}],"graph_snapshots":[{"event_id":"sha256:70f3627036b1ad565186036901f49c6d2a73eb7dfd741f516906abb201e139db","target":"graph","created_at":"2026-07-05T05:37:16Z","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/2301.13573/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Recent work has shown that Large Language Models (LLMs) can be incredibly effective for offline reinforcement learning (RL) by representing the traditional RL problem as a sequence modelling problem (Chen et al., 2021; Janner et al., 2021). However many of these methods only optimize for high returns, and may not extract much information from a diverse dataset of trajectories. Generalized Decision Transformers (GDTs) (Furuta et al., 2021) have shown that utilizing future trajectory information, in the form of information statistics, can help extract more information from offline trajectory dat","authors_text":"Sebastian Risi, Shyam Sudhakaran","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-01-31T11:52:46Z","title":"Skill Decision Transformer"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2301.13573","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":"sha256:1b3e2d57ae8c1a3b7d7463b49a551233b6c795e66f2ec135070ac468f655caef","target":"record","created_at":"2026-07-05T05:37:16Z","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":"61b7896ca16eab302301c728b8d426b5b6c7545279ac4752f9e29111ca2efe14","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-01-31T11:52:46Z","title_canon_sha256":"f802cab849f56e0edd4440a2851e8426d33ae936a9e6c9cf13c4614479212bfe"},"schema_version":"1.0","source":{"id":"2301.13573","kind":"arxiv","version":1}},"canonical_sha256":"1936d8c7b26aa6a9ff6d9cc2d8d60937e835862226e7443d97f4d59e4dcc0d0b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"1936d8c7b26aa6a9ff6d9cc2d8d60937e835862226e7443d97f4d59e4dcc0d0b","first_computed_at":"2026-07-05T05:37:16.276964Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:37:16.276964Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"4BRcEtYDqW+P6HVOyrQ5fZKWuDwP3wcuW2KD7Xwwh9FyhDD4SZoLIiiq87d5XfaKPfKeahyvDBLxBvFDGSHKAQ==","signature_status":"signed_v1","signed_at":"2026-07-05T05:37:16.277478Z","signed_message":"canonical_sha256_bytes"},"source_id":"2301.13573","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:1b3e2d57ae8c1a3b7d7463b49a551233b6c795e66f2ec135070ac468f655caef","sha256:70f3627036b1ad565186036901f49c6d2a73eb7dfd741f516906abb201e139db"],"state_sha256":"e882e1f9d0303a241d4c54d3091269fab7ad13af2dd9323ab041394d0411205a"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"D6VEP2mE8TEA/XXLmre2drR69GtFHo5rKW4KNsHY7CBx3lCr/d3Y/vCPWjKuowiqvHUHVBGYLJJka5XrHtyxCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-18T16:10:35.760747Z","bundle_sha256":"aab7df39a8c595872d5f186a8b58deb43b2349f57bcb426f6e5a492d1c900c8f"}}