{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:EPL3X3OAQBB3E4WJ6UTSXIOFHV","short_pith_number":"pith:EPL3X3OA","schema_version":"1.0","canonical_sha256":"23d7bbedc08043b272c9f5272ba1c53d603af88783a7186a540fe04b07f981b2","source":{"kind":"arxiv","id":"2606.19641","version":1},"attestation_state":"computed","paper":{"title":"Scaling Self-Play for End-to-End Driving","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.RO","authors_text":"Alaap Grandhi, Christopher Pal, Daphne Cornelisse, Eugene Vinitsky, Felix Heide, Liam Paull, Luke Rowe, Rodrigue de Schaetzen, Roger Girgis","submitted_at":"2026-06-17T22:39:11Z","abstract_excerpt":"End-to-end autonomous driving models are typically trained on offline human-demonstration datasets that provide limited state coverage and often no closed-loop feedback, making them prone to compounding errors when deployed in closed-loop and brittle to long-tail agent interactions. To overcome these limitations, we propose an alternative strategy for training end-to-end driving models: large-scale self-play directly from pixels in simulation. While prior self-play approaches have shown promising transfer to real-world driving, they typically assume vectorized Bird's-Eye-View (BEV) observation"},"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":"2606.19641","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2026-06-17T22:39:11Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"54a6ee8be9f194609cc3baec01235b4d472690f2e7bcf10b492f59deaed78cb8","abstract_canon_sha256":"1f3dda0b3f46184ea38997366fa758012c4c61ff8861c779a6bfd16d5a92c433"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-19T16:12:31.071148Z","signature_b64":"I3Wt5RyLGMwUzQv+7up5Ewvz27yo6DZLgSGrkp1gU5MwA5PTIV/vvPsE2D4uORrmHTqFhJV/ZM1dEiEsSjMKAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"23d7bbedc08043b272c9f5272ba1c53d603af88783a7186a540fe04b07f981b2","last_reissued_at":"2026-06-19T16:12:31.070801Z","signature_status":"signed_v1","first_computed_at":"2026-06-19T16:12:31.070801Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Scaling Self-Play for End-to-End Driving","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.RO","authors_text":"Alaap Grandhi, Christopher Pal, Daphne Cornelisse, Eugene Vinitsky, Felix Heide, Liam Paull, Luke Rowe, Rodrigue de Schaetzen, Roger Girgis","submitted_at":"2026-06-17T22:39:11Z","abstract_excerpt":"End-to-end autonomous driving models are typically trained on offline human-demonstration datasets that provide limited state coverage and often no closed-loop feedback, making them prone to compounding errors when deployed in closed-loop and brittle to long-tail agent interactions. To overcome these limitations, we propose an alternative strategy for training end-to-end driving models: large-scale self-play directly from pixels in simulation. While prior self-play approaches have shown promising transfer to real-world driving, they typically assume vectorized Bird's-Eye-View (BEV) observation"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.19641","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/2606.19641/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":"2606.19641","created_at":"2026-06-19T16:12:31.070864+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.19641v1","created_at":"2026-06-19T16:12:31.070864+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.19641","created_at":"2026-06-19T16:12:31.070864+00:00"},{"alias_kind":"pith_short_12","alias_value":"EPL3X3OAQBB3","created_at":"2026-06-19T16:12:31.070864+00:00"},{"alias_kind":"pith_short_16","alias_value":"EPL3X3OAQBB3E4WJ","created_at":"2026-06-19T16:12:31.070864+00:00"},{"alias_kind":"pith_short_8","alias_value":"EPL3X3OA","created_at":"2026-06-19T16:12:31.070864+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/EPL3X3OAQBB3E4WJ6UTSXIOFHV","json":"https://pith.science/pith/EPL3X3OAQBB3E4WJ6UTSXIOFHV.json","graph_json":"https://pith.science/api/pith-number/EPL3X3OAQBB3E4WJ6UTSXIOFHV/graph.json","events_json":"https://pith.science/api/pith-number/EPL3X3OAQBB3E4WJ6UTSXIOFHV/events.json","paper":"https://pith.science/paper/EPL3X3OA"},"agent_actions":{"view_html":"https://pith.science/pith/EPL3X3OAQBB3E4WJ6UTSXIOFHV","download_json":"https://pith.science/pith/EPL3X3OAQBB3E4WJ6UTSXIOFHV.json","view_paper":"https://pith.science/paper/EPL3X3OA","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.19641&json=true","fetch_graph":"https://pith.science/api/pith-number/EPL3X3OAQBB3E4WJ6UTSXIOFHV/graph.json","fetch_events":"https://pith.science/api/pith-number/EPL3X3OAQBB3E4WJ6UTSXIOFHV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/EPL3X3OAQBB3E4WJ6UTSXIOFHV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/EPL3X3OAQBB3E4WJ6UTSXIOFHV/action/storage_attestation","attest_author":"https://pith.science/pith/EPL3X3OAQBB3E4WJ6UTSXIOFHV/action/author_attestation","sign_citation":"https://pith.science/pith/EPL3X3OAQBB3E4WJ6UTSXIOFHV/action/citation_signature","submit_replication":"https://pith.science/pith/EPL3X3OAQBB3E4WJ6UTSXIOFHV/action/replication_record"}},"created_at":"2026-06-19T16:12:31.070864+00:00","updated_at":"2026-06-19T16:12:31.070864+00:00"}