{"as_of":"2026-08-08T17:53:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:bb9ccd7da9fc687c9e11d6c63b237de5128ea389f06a88254f3998badc79d977","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":10,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":10,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":10,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":10,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T15:23:18.124920Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-06-29T22:13:59.938139Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2405.16158","last_updated":"2024-12-03T08:42:49Z","snapshot_observed_at":"2026-08-07T12:11:17.938787Z","submitted_at":"2024-05-25T09:53:25Z","title":"Bigger, Regularized, Optimistic: scaling for compute and sample-efficient continuous control","version":3},"cited_work":{"arxiv_id":"2405.16158","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2405.16158","snapshot_observed_at":"2026-06-29T22:13:59.938139Z","title":"Bigger, regularized, optimistic: scaling for compute and sample-efficient con- tinuous control","venue":null,"work_id":"f46fb47c-8461-4c39-8958-4f63c280a7a5","year":2024},"citing_paper":{"arxiv_id":"2411.04832","last_updated":"2026-04-18T13:11:19Z","snapshot_observed_at":"2026-08-07T20:09:01.472991Z","submitted_at":"2024-11-07T16:13:54Z","title":"Plasticity Loss in Deep Reinforcement Learning: A Survey","version":3},"reference_index":83,"source":"pdf_text","source_observed_at":"2026-05-23T18:02:30.199552Z"},"links":{"cited_paper":"/paper/2405.16158","citing_paper":"/paper/2411.04832"},"observation_digest":"sha256:288806179cd4f749957438fa132f1c7e51e4ff5e1166ada00a0bf96ed3c07797","observation_id":"12df5705-2899-420a-b31a-a2d39af4fd04","resolution":{"observed_at":"2026-05-23T18:03:18.143673Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2405.16158","last_updated":"2024-12-03T08:42:49Z","snapshot_observed_at":"2026-08-07T12:11:17.938787Z","submitted_at":"2024-05-25T09:53:25Z","title":"Bigger, Regularized, Optimistic: scaling for compute and sample-efficient continuous control","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.16158","snapshot_observed_at":"2026-08-07T15:23:18.124920Z","title":"Nauman, M","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.15345","last_updated":"2025-05-23T11:18:10Z","snapshot_observed_at":"2026-08-07T20:34:34.327158Z","submitted_at":"2025-05-21T10:19:49Z","title":"Hadamax Encoding: Elevating Performance in Model-Free Atari","version":2},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-07T15:23:18.124920Z"},"links":{"cited_paper":"/paper/2405.16158","citing_paper":"/paper/2505.15345"},"observation_digest":"sha256:24234f3b49b2e73e55e172dbd9ffec603aa5612d32a956a8cdec34dae4110e4c","observation_id":"56e4cccc-23a8-45ef-814b-01cbfdb222ba","resolution":{"observed_at":"2026-08-07T15:23:18.124920Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.16158","last_updated":"2024-12-03T08:42:49Z","snapshot_observed_at":"2026-08-07T12:11:17.938787Z","submitted_at":"2024-05-25T09:53:25Z","title":"Bigger, Regularized, Optimistic: scaling for compute and sample-efficient continuous control","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.16158","snapshot_observed_at":"2026-08-07T12:58:51.287792Z","title":"Bigger, regularized, optimistic: scaling for compute and sample-efficient continuous control","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.23150","last_updated":"2025-05-29T06:41:45Z","snapshot_observed_at":"2026-08-07T23:49:30.072079Z","submitted_at":"2025-05-29T06:41:45Z","title":"Bigger, Regularized, Categorical: High-Capacity Value Functions are Efficient Multi-Task Learners","version":1},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-08-07T12:58:51.287792Z"},"links":{"cited_paper":"/paper/2405.16158","citing_paper":"/paper/2505.23150"},"observation_digest":"sha256:baf152b79c1585f7f48cfc7ee25c6a6a120aaad34fd5b4734875703021319fe5","observation_id":"b79eba20-6d7e-4ddb-b599-54112b534780","resolution":{"observed_at":"2026-08-07T12:58:51.287792Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.16158","last_updated":"2024-12-03T08:42:49Z","snapshot_observed_at":"2026-08-07T12:11:17.938787Z","submitted_at":"2024-05-25T09:53:25Z","title":"Bigger, Regularized, Optimistic: scaling for compute and sample-efficient continuous control","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.16158","snapshot_observed_at":"2026-08-07T00:32:51.919314Z","title":"Bigger, regularized, optimistic: scaling for compute and sample-efficient continuous control","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.13672","last_updated":"2025-06-16T16:30:00Z","snapshot_observed_at":"2026-08-07T00:25:29.162842Z","submitted_at":"2025-06-16T16:30:00Z","title":"The Courage to Stop: Overcoming Sunk Cost Fallacy in Deep Reinforcement Learning","version":1},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-07T00:32:51.919314Z"},"links":{"cited_paper":"/paper/2405.16158","citing_paper":"/paper/2506.13672"},"observation_digest":"sha256:e2b55d8d1132d0349306ada6dfb778558c4bd9e4f7fcb559bf7a413da8247314","observation_id":"0d1d9e27-5aed-4b82-a953-88d50f1a3aeb","resolution":{"observed_at":"2026-08-07T00:32:51.919314Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.16158","last_updated":"2024-12-03T08:42:49Z","snapshot_observed_at":"2026-08-07T12:11:17.938787Z","submitted_at":"2024-05-25T09:53:25Z","title":"Bigger, Regularized, Optimistic: scaling for compute and sample-efficient continuous control","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.16158","snapshot_observed_at":"2026-08-06T20:31:07.833263Z","title":"Bigger, regularized, optimistic: scaling for compute and sample-efficient continuous control","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.02712","last_updated":"2025-07-03T15:26:48Z","snapshot_observed_at":"2026-08-08T15:34:11.449065Z","submitted_at":"2025-07-03T15:26:48Z","title":"A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control","version":1},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-08-06T20:31:07.833263Z"},"links":{"cited_paper":"/paper/2405.16158","citing_paper":"/paper/2507.02712"},"observation_digest":"sha256:c1f4a736aaf348f4f2ad99ad047f676701d68c984a54392308046d1e653c97a8","observation_id":"06aacba1-9960-461d-af50-c9385de58593","resolution":{"observed_at":"2026-08-06T20:31:07.833263Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.16158","last_updated":"2024-12-03T08:42:49Z","snapshot_observed_at":"2026-08-07T12:11:17.938787Z","submitted_at":"2024-05-25T09:53:25Z","title":"Bigger, Regularized, Optimistic: scaling for compute and sample-efficient continuous control","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.16158","snapshot_observed_at":"2026-08-06T15:55:35.778737Z","title":"Bigger, regularized, optimistic: scaling for compute and sample-efficient continuous control, 2024 b","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.14736","last_updated":"2025-07-19T19:53:08Z","snapshot_observed_at":"2026-08-06T15:46:49.918611Z","submitted_at":"2025-07-19T19:53:08Z","title":"Balancing Expressivity and Robustness: Constrained Rational Activations for Reinforcement Learning","version":1},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-06T15:55:35.778737Z"},"links":{"cited_paper":"/paper/2405.16158","citing_paper":"/paper/2507.14736"},"observation_digest":"sha256:b68aac63f4fcbd433ad14149aaab138e8a1ae9633520f1e439dd40f2abfda2ca","observation_id":"7b5582cd-c7f1-41cc-98de-d5323d930bd3","resolution":{"observed_at":"2026-08-06T15:55:35.778737Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.16158","last_updated":"2024-12-03T08:42:49Z","snapshot_observed_at":"2026-08-07T12:11:17.938787Z","submitted_at":"2024-05-25T09:53:25Z","title":"Bigger, Regularized, Optimistic: scaling for compute and sample-efficient continuous control","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.16158","snapshot_observed_at":"2026-08-06T05:44:15.127132Z","title":"Bigger, regularized, optimistic: scaling for compute and sample-efficient continuous control","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.01329","last_updated":"2025-08-02T11:40:26Z","snapshot_observed_at":"2026-08-06T05:44:05.230001Z","submitted_at":"2025-08-02T11:40:26Z","title":"Is Exploration or Optimization the Problem for Deep Reinforcement Learning?","version":1},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-08-06T05:44:15.127132Z"},"links":{"cited_paper":"/paper/2405.16158","citing_paper":"/paper/2508.01329"},"observation_digest":"sha256:37eaa75fba6682ffb6f6a14e5be69583f2afe774389234adc3f35c5459b235ec","observation_id":"64d31f03-dd55-440d-a529-5d9f1229d620","resolution":{"observed_at":"2026-08-06T05:44:15.127132Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.16158","last_updated":"2024-12-03T08:42:49Z","snapshot_observed_at":"2026-08-07T12:11:17.938787Z","submitted_at":"2024-05-25T09:53:25Z","title":"Bigger, Regularized, Optimistic: scaling for compute and sample-efficient continuous control","version":3},"cited_work":{"arxiv_id":"2405.16158","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2405.16158","snapshot_observed_at":"2026-06-29T22:13:59.938139Z","title":"Bigger, regularized, optimistic: scaling for compute and sample-efficient con- tinuous control","venue":null,"work_id":"f46fb47c-8461-4c39-8958-4f63c280a7a5","year":2024},"citing_paper":{"arxiv_id":"2604.04539","last_updated":"2026-05-15T07:15:36Z","snapshot_observed_at":"2026-08-02T17:13:51.910717Z","submitted_at":"2026-04-06T09:03:41Z","title":"FlashSAC: Fast and Stable Off-Policy Reinforcement Learning for High-Dimensional Robot Control","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-05-10T20:04:56.512544Z"},"links":{"cited_paper":"/paper/2405.16158","citing_paper":"/paper/2604.04539"},"observation_digest":"sha256:a91e0f02e0b1e312e7f55ae4ebf8402ac02074fa37386f4cac5612cfcb74c540","observation_id":"9206bf9e-eece-4d1f-bee0-19b664cfdc70","resolution":{"observed_at":"2026-05-10T22:15:49.961728Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2405.16158","last_updated":"2024-12-03T08:42:49Z","snapshot_observed_at":"2026-08-07T12:11:17.938787Z","submitted_at":"2024-05-25T09:53:25Z","title":"Bigger, Regularized, Optimistic: scaling for compute and sample-efficient continuous control","version":3},"cited_work":{"arxiv_id":"2405.16158","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2405.16158","snapshot_observed_at":"2026-06-29T22:13:59.938139Z","title":"Bigger, regularized, optimistic: scaling for compute and sample-efficient con- tinuous control","venue":null,"work_id":"f46fb47c-8461-4c39-8958-4f63c280a7a5","year":2024},"citing_paper":{"arxiv_id":"2604.04539","last_updated":"2026-05-15T07:15:36Z","snapshot_observed_at":"2026-08-02T17:13:51.910717Z","submitted_at":"2026-04-06T09:03:41Z","title":"FlashSAC: Fast and Stable Off-Policy Reinforcement Learning for High-Dimensional Robot Control","version":2},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-05-19T17:08:31.770889Z"},"links":{"cited_paper":"/paper/2405.16158","citing_paper":"/paper/2604.04539"},"observation_digest":"sha256:6593ac54f36f1828b16280baf72b29e022598202dde8c1c430c051c776866fc7","observation_id":"ad519fa5-9a53-4e6c-a75f-0e60c6853a49","resolution":{"observed_at":"2026-05-19T17:12:41.353864Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2405.16158","last_updated":"2024-12-03T08:42:49Z","snapshot_observed_at":"2026-08-07T12:11:17.938787Z","submitted_at":"2024-05-25T09:53:25Z","title":"Bigger, Regularized, Optimistic: scaling for compute and sample-efficient continuous control","version":3},"cited_work":{"arxiv_id":"2405.16158","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2405.16158","snapshot_observed_at":"2026-06-29T22:13:59.938139Z","title":"Bigger, regularized, optimistic: scaling for compute and sample-efficient con- tinuous control","venue":null,"work_id":"f46fb47c-8461-4c39-8958-4f63c280a7a5","year":2024},"citing_paper":{"arxiv_id":"2605.25477","last_updated":"2026-05-25T06:31:03Z","snapshot_observed_at":"2026-08-06T17:11:50.083428Z","submitted_at":"2026-05-25T06:31:03Z","title":"EXPO-FT: Sample-Efficient Reinforcement Learning Finetuning for Vision-Language-Action Models","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-06-29T22:10:08.682307Z"},"links":{"cited_paper":"/paper/2405.16158","citing_paper":"/paper/2605.25477"},"observation_digest":"sha256:ca59b26ab7a063aaadbb46401a3d411eef4325f1611390f298827104fc975ac9","observation_id":"0023601c-df6c-4044-8a74-e34949ed78c3","resolution":{"observed_at":"2026-06-29T22:13:59.939901Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2405.16158/citation-record","integrity":"/paper/2405.16158/integrity","json":"/paper/2405.16158/citation-record.json","paper":"/paper/2405.16158"},"outbound":[],"paper":{"arxiv_id":"2405.16158","last_updated":"2024-12-03T08:42:49Z","latest_version":3,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-07T12:11:17.938787Z","submitted_at":"2024-05-25T09:53:25Z","title":"Bigger, Regularized, Optimistic: scaling for compute and sample-efficient continuous control"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 inbound Pith citation observations for arXiv:2405.16158."}