{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:GSJJVMJVBII3GMMEPUPLOLX26W","short_pith_number":"pith:GSJJVMJV","schema_version":"1.0","canonical_sha256":"34929ab1350a11b331847d1eb72efaf59d6f63db20bc2a7a53802740c02434b3","source":{"kind":"arxiv","id":"2507.05876","version":1},"attestation_state":"computed","paper":{"title":"OLAF: Programmable Data Plane Acceleration for Asynchronous Distributed Reinforcement Learning","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AR"],"primary_cat":"cs.NI","authors_text":"Amr Rizk, Anam Tahir, Firas Khamis, Michael Zink, Mina Tahmasbi Arashloo, Nehal Baganal Krishna","submitted_at":"2025-07-08T10:59:56Z","abstract_excerpt":"Asynchronous Distributed Reinforcement Learning (DRL) can suffer from degraded convergence when model updates become stale, often the result of network congestion and packet loss during large-scale training. This work introduces a network data-plane acceleration architecture that mitigates such staleness by enabling inline processing of DRL model updates as they traverse the accelerator engine. To this end, we design and prototype a novel queueing mechanism that opportunistically combines compatible updates sharing a network element, reducing redundant traffic and preserving update utility. Co"},"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":"2507.05876","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.NI","submitted_at":"2025-07-08T10:59:56Z","cross_cats_sorted":["cs.AR"],"title_canon_sha256":"52ddd657ed1a91ed13f501cf2ee7665aa6658d9203851d23713f993c51283bcd","abstract_canon_sha256":"404b0c702c9c7ba7ddcc0e04fcec861bb7bfb13b3083582280c727f049798728"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:33:37.529720Z","signature_b64":"6IbbwOshose/B+ey26Wt7DGj4c02mHSzbrnEeCBKW0fJfq5SY0cjTRTMZfQpUaGRbOdaOUL96Q69VvEdDgaGCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"34929ab1350a11b331847d1eb72efaf59d6f63db20bc2a7a53802740c02434b3","last_reissued_at":"2026-07-05T11:33:37.529232Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:33:37.529232Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"OLAF: Programmable Data Plane Acceleration for Asynchronous Distributed Reinforcement Learning","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AR"],"primary_cat":"cs.NI","authors_text":"Amr Rizk, Anam Tahir, Firas Khamis, Michael Zink, Mina Tahmasbi Arashloo, Nehal Baganal Krishna","submitted_at":"2025-07-08T10:59:56Z","abstract_excerpt":"Asynchronous Distributed Reinforcement Learning (DRL) can suffer from degraded convergence when model updates become stale, often the result of network congestion and packet loss during large-scale training. This work introduces a network data-plane acceleration architecture that mitigates such staleness by enabling inline processing of DRL model updates as they traverse the accelerator engine. To this end, we design and prototype a novel queueing mechanism that opportunistically combines compatible updates sharing a network element, reducing redundant traffic and preserving update utility. Co"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.05876","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/2507.05876/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":"2507.05876","created_at":"2026-07-05T11:33:37.529290+00:00"},{"alias_kind":"arxiv_version","alias_value":"2507.05876v1","created_at":"2026-07-05T11:33:37.529290+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.05876","created_at":"2026-07-05T11:33:37.529290+00:00"},{"alias_kind":"pith_short_12","alias_value":"GSJJVMJVBII3","created_at":"2026-07-05T11:33:37.529290+00:00"},{"alias_kind":"pith_short_16","alias_value":"GSJJVMJVBII3GMME","created_at":"2026-07-05T11:33:37.529290+00:00"},{"alias_kind":"pith_short_8","alias_value":"GSJJVMJV","created_at":"2026-07-05T11:33:37.529290+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/GSJJVMJVBII3GMMEPUPLOLX26W","json":"https://pith.science/pith/GSJJVMJVBII3GMMEPUPLOLX26W.json","graph_json":"https://pith.science/api/pith-number/GSJJVMJVBII3GMMEPUPLOLX26W/graph.json","events_json":"https://pith.science/api/pith-number/GSJJVMJVBII3GMMEPUPLOLX26W/events.json","paper":"https://pith.science/paper/GSJJVMJV"},"agent_actions":{"view_html":"https://pith.science/pith/GSJJVMJVBII3GMMEPUPLOLX26W","download_json":"https://pith.science/pith/GSJJVMJVBII3GMMEPUPLOLX26W.json","view_paper":"https://pith.science/paper/GSJJVMJV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2507.05876&json=true","fetch_graph":"https://pith.science/api/pith-number/GSJJVMJVBII3GMMEPUPLOLX26W/graph.json","fetch_events":"https://pith.science/api/pith-number/GSJJVMJVBII3GMMEPUPLOLX26W/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GSJJVMJVBII3GMMEPUPLOLX26W/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GSJJVMJVBII3GMMEPUPLOLX26W/action/storage_attestation","attest_author":"https://pith.science/pith/GSJJVMJVBII3GMMEPUPLOLX26W/action/author_attestation","sign_citation":"https://pith.science/pith/GSJJVMJVBII3GMMEPUPLOLX26W/action/citation_signature","submit_replication":"https://pith.science/pith/GSJJVMJVBII3GMMEPUPLOLX26W/action/replication_record"}},"created_at":"2026-07-05T11:33:37.529290+00:00","updated_at":"2026-07-05T11:33:37.529290+00:00"}