{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:UABKUMIBY6ZGBXN5NFEX4JGCN5","short_pith_number":"pith:UABKUMIB","canonical_record":{"source":{"id":"2307.09423","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-07-18T16:43:03Z","cross_cats_sorted":["cs.AI","stat.ML"],"title_canon_sha256":"0d19ea633cd777e99f53d6b5edeb01f682a8335dd6d77544732070e297c3562a","abstract_canon_sha256":"4b94984f0be4c34b23120b0197eb93b9d194cbb8364af318227cbe73c028e5db"},"schema_version":"1.0"},"canonical_sha256":"a002aa3101c7b260ddbd69497e24c26f4d189337d25891c930e417ff8c07b868","source":{"kind":"arxiv","id":"2307.09423","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2307.09423","created_at":"2026-07-05T09:51:28Z"},{"alias_kind":"arxiv_version","alias_value":"2307.09423v3","created_at":"2026-07-05T09:51:28Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2307.09423","created_at":"2026-07-05T09:51:28Z"},{"alias_kind":"pith_short_12","alias_value":"UABKUMIBY6ZG","created_at":"2026-07-05T09:51:28Z"},{"alias_kind":"pith_short_16","alias_value":"UABKUMIBY6ZGBXN5","created_at":"2026-07-05T09:51:28Z"},{"alias_kind":"pith_short_8","alias_value":"UABKUMIB","created_at":"2026-07-05T09:51:28Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:UABKUMIBY6ZGBXN5NFEX4JGCN5","target":"record","payload":{"canonical_record":{"source":{"id":"2307.09423","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-07-18T16:43:03Z","cross_cats_sorted":["cs.AI","stat.ML"],"title_canon_sha256":"0d19ea633cd777e99f53d6b5edeb01f682a8335dd6d77544732070e297c3562a","abstract_canon_sha256":"4b94984f0be4c34b23120b0197eb93b9d194cbb8364af318227cbe73c028e5db"},"schema_version":"1.0"},"canonical_sha256":"a002aa3101c7b260ddbd69497e24c26f4d189337d25891c930e417ff8c07b868","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:51:28.627385Z","signature_b64":"jQx9p3OLXiehe/ycAJe/WeSOCRofUFeD8Aw+q/aIldqnU2a+IMTN/QhgmUS/6K07uul9ojJGUHcvEzfI5AMVCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a002aa3101c7b260ddbd69497e24c26f4d189337d25891c930e417ff8c07b868","last_reissued_at":"2026-07-05T09:51:28.626922Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:51:28.626922Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2307.09423","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T09:51:28Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Irlh8StHYmx7EEZeha9wYUSIW3y1Y67PTAzBuE0Sbx/O7nM/AEoTM+5cK2KFaEi4pFzFIXokgCdFOy8b0rHxCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-22T14:36:24.694595Z"},"content_sha256":"db49de7785abf35b5fe9f5a6c268e67a12b72017527f9bbd2326ca8a7c59ab93","schema_version":"1.0","event_id":"sha256:db49de7785abf35b5fe9f5a6c268e67a12b72017527f9bbd2326ca8a7c59ab93"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:UABKUMIBY6ZGBXN5NFEX4JGCN5","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Scaling Laws for Imitation Learning in Single-Agent Games","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","stat.ML"],"primary_cat":"cs.LG","authors_text":"Dean Foster, Dhruv Madeka, Jens Tuyls, Kari Torkkola, Karthik Narasimhan, Sham Kakade","submitted_at":"2023-07-18T16:43:03Z","abstract_excerpt":"Imitation Learning (IL) is one of the most widely used methods in machine learning. Yet, many works find it is often unable to fully recover the underlying expert behavior, even in constrained environments like single-agent games. However, none of these works deeply investigate the role of scaling up the model and data size. Inspired by recent work in Natural Language Processing (NLP) where \"scaling up\" has resulted in increasingly more capable LLMs, we investigate whether carefully scaling up model and data size can bring similar improvements in the imitation learning setting for single-agent"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2307.09423","kind":"arxiv","version":3},"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/2307.09423/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":"2026-07-05T09:51:28Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"qYa7/tzaIRPESqKnTr9xDidCvY5zQEelU9dJAr/N52QU5TSSpNLGLrSBSQuZqrr+8t4PKlaS3ANso0jvswJZDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-22T14:36:24.695087Z"},"content_sha256":"dcab82ea8d876541940e850a8efd86bbbfa861f34256653b2d40536ea24bbd42","schema_version":"1.0","event_id":"sha256:dcab82ea8d876541940e850a8efd86bbbfa861f34256653b2d40536ea24bbd42"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/UABKUMIBY6ZGBXN5NFEX4JGCN5/bundle.json","state_url":"https://pith.science/pith/UABKUMIBY6ZGBXN5NFEX4JGCN5/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/UABKUMIBY6ZGBXN5NFEX4JGCN5/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":"2026-08-22T14:36:24Z","links":{"resolver":"https://pith.science/pith/UABKUMIBY6ZGBXN5NFEX4JGCN5","bundle":"https://pith.science/pith/UABKUMIBY6ZGBXN5NFEX4JGCN5/bundle.json","state":"https://pith.science/pith/UABKUMIBY6ZGBXN5NFEX4JGCN5/state.json","well_known_bundle":"https://pith.science/.well-known/pith/UABKUMIBY6ZGBXN5NFEX4JGCN5/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:UABKUMIBY6ZGBXN5NFEX4JGCN5","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":"4b94984f0be4c34b23120b0197eb93b9d194cbb8364af318227cbe73c028e5db","cross_cats_sorted":["cs.AI","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-07-18T16:43:03Z","title_canon_sha256":"0d19ea633cd777e99f53d6b5edeb01f682a8335dd6d77544732070e297c3562a"},"schema_version":"1.0","source":{"id":"2307.09423","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2307.09423","created_at":"2026-07-05T09:51:28Z"},{"alias_kind":"arxiv_version","alias_value":"2307.09423v3","created_at":"2026-07-05T09:51:28Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2307.09423","created_at":"2026-07-05T09:51:28Z"},{"alias_kind":"pith_short_12","alias_value":"UABKUMIBY6ZG","created_at":"2026-07-05T09:51:28Z"},{"alias_kind":"pith_short_16","alias_value":"UABKUMIBY6ZGBXN5","created_at":"2026-07-05T09:51:28Z"},{"alias_kind":"pith_short_8","alias_value":"UABKUMIB","created_at":"2026-07-05T09:51:28Z"}],"graph_snapshots":[{"event_id":"sha256:dcab82ea8d876541940e850a8efd86bbbfa861f34256653b2d40536ea24bbd42","target":"graph","created_at":"2026-07-05T09:51:28Z","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/2307.09423/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Imitation Learning (IL) is one of the most widely used methods in machine learning. Yet, many works find it is often unable to fully recover the underlying expert behavior, even in constrained environments like single-agent games. However, none of these works deeply investigate the role of scaling up the model and data size. Inspired by recent work in Natural Language Processing (NLP) where \"scaling up\" has resulted in increasingly more capable LLMs, we investigate whether carefully scaling up model and data size can bring similar improvements in the imitation learning setting for single-agent","authors_text":"Dean Foster, Dhruv Madeka, Jens Tuyls, Kari Torkkola, Karthik Narasimhan, Sham Kakade","cross_cats":["cs.AI","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-07-18T16:43:03Z","title":"Scaling Laws for Imitation Learning in Single-Agent Games"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2307.09423","kind":"arxiv","version":3},"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:db49de7785abf35b5fe9f5a6c268e67a12b72017527f9bbd2326ca8a7c59ab93","target":"record","created_at":"2026-07-05T09:51:28Z","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":"4b94984f0be4c34b23120b0197eb93b9d194cbb8364af318227cbe73c028e5db","cross_cats_sorted":["cs.AI","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-07-18T16:43:03Z","title_canon_sha256":"0d19ea633cd777e99f53d6b5edeb01f682a8335dd6d77544732070e297c3562a"},"schema_version":"1.0","source":{"id":"2307.09423","kind":"arxiv","version":3}},"canonical_sha256":"a002aa3101c7b260ddbd69497e24c26f4d189337d25891c930e417ff8c07b868","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a002aa3101c7b260ddbd69497e24c26f4d189337d25891c930e417ff8c07b868","first_computed_at":"2026-07-05T09:51:28.626922Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:51:28.626922Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"jQx9p3OLXiehe/ycAJe/WeSOCRofUFeD8Aw+q/aIldqnU2a+IMTN/QhgmUS/6K07uul9ojJGUHcvEzfI5AMVCw==","signature_status":"signed_v1","signed_at":"2026-07-05T09:51:28.627385Z","signed_message":"canonical_sha256_bytes"},"source_id":"2307.09423","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:db49de7785abf35b5fe9f5a6c268e67a12b72017527f9bbd2326ca8a7c59ab93","sha256:dcab82ea8d876541940e850a8efd86bbbfa861f34256653b2d40536ea24bbd42"],"state_sha256":"2bfccc229ebf042e42d4047548da3a4582be6788450c564084160330dc4cef34"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"g1VnsiG2nK1mYHQZh2i5M8guiSjbsGMJvei+ky/FpgzXWU/lyBuDkwHu/l+ya88LxeeU1ZwknYlK8z38bDs4Bw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-22T14:36:24.699446Z","bundle_sha256":"76ef4686cb86e1b5babf2f25480b6f66401ea2eb13ed59f5b370a50115b48213"}}