{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:KW72DWTG73RRFQU65X37GJRPH6","short_pith_number":"pith:KW72DWTG","schema_version":"1.0","canonical_sha256":"55bfa1da66fee312c29eedf7f3262f3f8e9b27f6501bbb2be2105cf69243ec64","source":{"kind":"arxiv","id":"2608.07870","version":1},"attestation_state":"computed","paper":{"title":"V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.RO"],"primary_cat":"cs.LG","authors_text":"Aaron Courville, Byungkun Lee, Clare Lyle, Donghu Kim, Hojoon Lee, Jaegul Choo, Johan Obando-Ceron, Pablo Samuel Castro, Youngdo Lee","submitted_at":"2026-08-08T02:44:43Z","abstract_excerpt":"Improving sample efficiency remains a core challenge in reinforcement learning (RL), especially in real-world settings like robotics, where data collection is costly. This challenge is pronounced in visual RL, where high-dimensional inputs often obscure learning signals. While prior work in visual RL has focused on algorithmic solutions, such as better dynamics models or exploration strategies, recent advances in state-based RL show that architectural design alone can lead to significant gains in sample efficiency. This raises an important question: Can these architectural principles transfer "},"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":"2608.07870","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2026-08-08T02:44:43Z","cross_cats_sorted":["cs.RO"],"title_canon_sha256":"c5d7618c1a66ae385645fcd0ac1b5eb696e6c3dd647201776e4a436d0f8c81d3","abstract_canon_sha256":"f1de72f7184f7dcb6b0fecad7d3f5cf9563ca91e3aadaa977b8bcc28b9adfc67"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-08-11T01:18:47.017211Z","signature_b64":"+ago8kKtn2GY1LthB+YB2ND8PK2zUUL4GywmsT9SNUW7MFL0ucK3DGiqv4DTs3++y1i03jrXAUe9z6eKDoE6Cw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"55bfa1da66fee312c29eedf7f3262f3f8e9b27f6501bbb2be2105cf69243ec64","last_reissued_at":"2026-08-11T01:18:47.013867Z","signature_status":"signed_v1","first_computed_at":"2026-08-11T01:18:47.013867Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.RO"],"primary_cat":"cs.LG","authors_text":"Aaron Courville, Byungkun Lee, Clare Lyle, Donghu Kim, Hojoon Lee, Jaegul Choo, Johan Obando-Ceron, Pablo Samuel Castro, Youngdo Lee","submitted_at":"2026-08-08T02:44:43Z","abstract_excerpt":"Improving sample efficiency remains a core challenge in reinforcement learning (RL), especially in real-world settings like robotics, where data collection is costly. This challenge is pronounced in visual RL, where high-dimensional inputs often obscure learning signals. While prior work in visual RL has focused on algorithmic solutions, such as better dynamics models or exploration strategies, recent advances in state-based RL show that architectural design alone can lead to significant gains in sample efficiency. This raises an important question: Can these architectural principles transfer "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2608.07870","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/2608.07870/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":"2608.07870","created_at":"2026-08-11T01:18:47.015155+00:00"},{"alias_kind":"arxiv_version","alias_value":"2608.07870v1","created_at":"2026-08-11T01:18:47.015155+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2608.07870","created_at":"2026-08-11T01:18:47.015155+00:00"},{"alias_kind":"pith_short_12","alias_value":"KW72DWTG73RR","created_at":"2026-08-11T01:18:47.015155+00:00"},{"alias_kind":"pith_short_16","alias_value":"KW72DWTG73RRFQU6","created_at":"2026-08-11T01:18:47.015155+00:00"},{"alias_kind":"pith_short_8","alias_value":"KW72DWTG","created_at":"2026-08-11T01:18:47.015155+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/KW72DWTG73RRFQU65X37GJRPH6","json":"https://pith.science/pith/KW72DWTG73RRFQU65X37GJRPH6.json","graph_json":"https://pith.science/api/pith-number/KW72DWTG73RRFQU65X37GJRPH6/graph.json","events_json":"https://pith.science/api/pith-number/KW72DWTG73RRFQU65X37GJRPH6/events.json","paper":"https://pith.science/paper/KW72DWTG"},"agent_actions":{"view_html":"https://pith.science/pith/KW72DWTG73RRFQU65X37GJRPH6","download_json":"https://pith.science/pith/KW72DWTG73RRFQU65X37GJRPH6.json","view_paper":"https://pith.science/paper/KW72DWTG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2608.07870&json=true","fetch_graph":"https://pith.science/api/pith-number/KW72DWTG73RRFQU65X37GJRPH6/graph.json","fetch_events":"https://pith.science/api/pith-number/KW72DWTG73RRFQU65X37GJRPH6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KW72DWTG73RRFQU65X37GJRPH6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KW72DWTG73RRFQU65X37GJRPH6/action/storage_attestation","attest_author":"https://pith.science/pith/KW72DWTG73RRFQU65X37GJRPH6/action/author_attestation","sign_citation":"https://pith.science/pith/KW72DWTG73RRFQU65X37GJRPH6/action/citation_signature","submit_replication":"https://pith.science/pith/KW72DWTG73RRFQU65X37GJRPH6/action/replication_record"}},"created_at":"2026-08-11T01:18:47.015155+00:00","updated_at":"2026-08-11T01:18:47.015155+00:00"}