{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:5ATEC7MVVMUT47XMB2L3OOSAIM","short_pith_number":"pith:5ATEC7MV","schema_version":"1.0","canonical_sha256":"e826417d95ab293e7eec0e97b73a40432a0bd39accb9065ddc8b2b13068f6042","source":{"kind":"arxiv","id":"2407.18840","version":1},"attestation_state":"computed","paper":{"title":"The Cross-environment Hyperparameter Setting Benchmark for Reinforcement Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Adam White, Andrew Patterson, Martha White, Raksha Kumaraswamy, Samuel Neumann","submitted_at":"2024-07-26T16:04:40Z","abstract_excerpt":"This paper introduces a new empirical methodology, the Cross-environment Hyperparameter Setting Benchmark, that compares RL algorithms across environments using a single hyperparameter setting, encouraging algorithmic development which is insensitive to hyperparameters. We demonstrate that this benchmark is robust to statistical noise and obtains qualitatively similar results across repeated applications, even when using few samples. This robustness makes the benchmark computationally cheap to apply, allowing statistically sound insights at low cost. We demonstrate two example instantiations o"},"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":"2407.18840","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-07-26T16:04:40Z","cross_cats_sorted":[],"title_canon_sha256":"373cd43082f4e7a4208a0a806e3d247f5fcfa764a1ee923cfd8da6a57663f5a3","abstract_canon_sha256":"d040f5f56c51dbaba2da5b49fe5f3a49600719152828e794c58e9a8f04447172"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:48:55.379253Z","signature_b64":"T8dFmHdNf+LVusN0UaaDmAkZ9uHevvQ2jqfKn+IjBw9V+v039G36AxmbKDoFQ114y6ZLpajGmLqN/4POwPQNBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e826417d95ab293e7eec0e97b73a40432a0bd39accb9065ddc8b2b13068f6042","last_reissued_at":"2026-07-05T08:48:55.378832Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:48:55.378832Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"The Cross-environment Hyperparameter Setting Benchmark for Reinforcement Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Adam White, Andrew Patterson, Martha White, Raksha Kumaraswamy, Samuel Neumann","submitted_at":"2024-07-26T16:04:40Z","abstract_excerpt":"This paper introduces a new empirical methodology, the Cross-environment Hyperparameter Setting Benchmark, that compares RL algorithms across environments using a single hyperparameter setting, encouraging algorithmic development which is insensitive to hyperparameters. We demonstrate that this benchmark is robust to statistical noise and obtains qualitatively similar results across repeated applications, even when using few samples. This robustness makes the benchmark computationally cheap to apply, allowing statistically sound insights at low cost. We demonstrate two example instantiations o"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.18840","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/2407.18840/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":"2407.18840","created_at":"2026-07-05T08:48:55.378892+00:00"},{"alias_kind":"arxiv_version","alias_value":"2407.18840v1","created_at":"2026-07-05T08:48:55.378892+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.18840","created_at":"2026-07-05T08:48:55.378892+00:00"},{"alias_kind":"pith_short_12","alias_value":"5ATEC7MVVMUT","created_at":"2026-07-05T08:48:55.378892+00:00"},{"alias_kind":"pith_short_16","alias_value":"5ATEC7MVVMUT47XM","created_at":"2026-07-05T08:48:55.378892+00:00"},{"alias_kind":"pith_short_8","alias_value":"5ATEC7MV","created_at":"2026-07-05T08:48:55.378892+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/5ATEC7MVVMUT47XMB2L3OOSAIM","json":"https://pith.science/pith/5ATEC7MVVMUT47XMB2L3OOSAIM.json","graph_json":"https://pith.science/api/pith-number/5ATEC7MVVMUT47XMB2L3OOSAIM/graph.json","events_json":"https://pith.science/api/pith-number/5ATEC7MVVMUT47XMB2L3OOSAIM/events.json","paper":"https://pith.science/paper/5ATEC7MV"},"agent_actions":{"view_html":"https://pith.science/pith/5ATEC7MVVMUT47XMB2L3OOSAIM","download_json":"https://pith.science/pith/5ATEC7MVVMUT47XMB2L3OOSAIM.json","view_paper":"https://pith.science/paper/5ATEC7MV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2407.18840&json=true","fetch_graph":"https://pith.science/api/pith-number/5ATEC7MVVMUT47XMB2L3OOSAIM/graph.json","fetch_events":"https://pith.science/api/pith-number/5ATEC7MVVMUT47XMB2L3OOSAIM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5ATEC7MVVMUT47XMB2L3OOSAIM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5ATEC7MVVMUT47XMB2L3OOSAIM/action/storage_attestation","attest_author":"https://pith.science/pith/5ATEC7MVVMUT47XMB2L3OOSAIM/action/author_attestation","sign_citation":"https://pith.science/pith/5ATEC7MVVMUT47XMB2L3OOSAIM/action/citation_signature","submit_replication":"https://pith.science/pith/5ATEC7MVVMUT47XMB2L3OOSAIM/action/replication_record"}},"created_at":"2026-07-05T08:48:55.378892+00:00","updated_at":"2026-07-05T08:48:55.378892+00:00"}