{"as_of":"2026-08-10T07:36:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:c6a3192b1fc69a5d2c3bff73238712704ad6a4832a154cb7bd36686bb0f3e94b","coverage":[{"denominator":6,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":6,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-03T06:26:50.470669Z","state":"measured"},{"denominator":7,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":7,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-10T02:55:22.577012Z","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":"2026-05-11T12:46:27.898232Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2601.23075","last_updated":"2026-06-23T21:44:31Z","snapshot_observed_at":"2026-08-03T06:17:20.457387Z","submitted_at":"2026-01-30T15:24:34Z","title":"RN-D: Discretized Categorical Actors for On-Policy Reinforcement Learning","version":2},"cited_work":{"arxiv_id":"2601.23075","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2601.23075","snapshot_observed_at":"2026-06-25T01:17:49.776384Z","title":"Rn-d: Discretized categorical actors with regularized networks for on-policy reinforcement learning","venue":null,"work_id":"9812dd64-9b5c-4ffa-93f3-7031427d166d","year":2026},"citing_paper":{"arxiv_id":"2604.18978","last_updated":"2026-05-07T15:47:31Z","snapshot_observed_at":"2026-08-08T15:22:09.895907Z","submitted_at":"2026-04-21T01:59:54Z","title":"Low-Rank Adaptation for Critic Learning in Off-Policy Reinforcement Learning","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-05-10T02:55:22.577012Z"},"links":{"cited_paper":"/paper/2601.23075","citing_paper":"/paper/2604.18978"},"observation_digest":"sha256:ecf55a33a4b140ae0f728bd3c89f758d788799d08f967cdfa1876f7d501d0761","observation_id":"4cd33387-6be2-4374-aaf0-7d4ee76a0ec4","resolution":{"observed_at":"2026-06-25T01:17:49.776384Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2601.23075/citation-record","integrity":"/paper/2601.23075/integrity","json":"/paper/2601.23075/citation-record.json","paper":"/paper/2601.23075"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T06:26:50.403340Z","title":"mean).Let πc(a|s) =N(µ,Σ) with fixed diagonal Σ = Diag(σ2) and parameter µ∈R m","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2601.23075","last_updated":"2026-06-23T21:44:31Z","snapshot_observed_at":"2026-08-03T06:17:20.457387Z","submitted_at":"2026-01-30T15:24:34Z","title":"RN-D: Discretized Categorical Actors for On-Policy Reinforcement Learning","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-03T06:26:50.403340Z"},"links":{"citing_paper":"/paper/2601.23075"},"observation_digest":"sha256:89b413fd1dc84fdb71f6fcf57e29e8b11aec1e1652d71805b32590a093bc9bad","observation_id":"96a2db2e-3d93-434d-9532-4c0d803f1999","resolution":{"observed_at":"2026-08-03T06:26:50.403340Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T06:26:50.470669Z","title":"logits).Consider one action dimension i with logits zi ∈R K and softmax probabilities pi = softmax(zi)∈∆ K−1","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2601.23075","last_updated":"2026-06-23T21:44:31Z","snapshot_observed_at":"2026-08-03T06:17:20.457387Z","submitted_at":"2026-01-30T15:24:34Z","title":"RN-D: Discretized Categorical Actors for On-Policy Reinforcement Learning","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-03T06:26:50.470669Z"},"links":{"citing_paper":"/paper/2601.23075"},"observation_digest":"sha256:8212eff848d0d0d5e0624b5bf652b04cb70a3a9802dc55722c88af7e79940acb","observation_id":"3c458954-539e-488f-a63b-79c5db8e1de3","resolution":{"observed_at":"2026-08-03T06:26:50.470669Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.09754","last_updated":"2025-05-29T15:02:38Z","snapshot_observed_at":"2026-08-10T03:59:03.902801Z","submitted_at":"2024-10-13T07:20:53Z","title":"SimBa: Simplicity Bias for Scaling Up Parameters in Deep Reinforcement Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.09754","snapshot_observed_at":"2026-08-03T06:26:49.906939Z","title":"Lee, H., Lee, Y ., Seno, T., Kim, D., Stone, P., and Choo, J","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2601.23075","last_updated":"2026-06-23T21:44:31Z","snapshot_observed_at":"2026-08-03T06:17:20.457387Z","submitted_at":"2026-01-30T15:24:34Z","title":"RN-D: Discretized Categorical Actors for On-Policy Reinforcement Learning","version":2},"reference_index":456,"source":"pdf_text","source_observed_at":"2026-08-03T06:26:49.906939Z"},"links":{"cited_paper":"/paper/2410.09754","citing_paper":"/paper/2601.23075"},"observation_digest":"sha256:fd6c7763be4ad55eba4033e41a52cdc3444b63e565fd55d4f9fa1041669ed4ec","observation_id":"789c6ba2-c50b-4d84-b762-c08c2fed956e","resolution":{"observed_at":"2026-08-03T06:26:49.906939Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.03950","last_updated":"2024-03-06T18:55:47Z","snapshot_observed_at":"2026-07-06T17:40:37.837709Z","submitted_at":"2024-03-06T18:55:47Z","title":"Stop Regressing: Training Value Functions via Classification for Scalable Deep RL","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.03950","snapshot_observed_at":"2026-08-03T06:26:49.758326Z","title":"Cobbe, K","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2601.23075","last_updated":"2026-06-23T21:44:31Z","snapshot_observed_at":"2026-08-03T06:17:20.457387Z","submitted_at":"2026-01-30T15:24:34Z","title":"RN-D: Discretized Categorical Actors for On-Policy Reinforcement Learning","version":2},"reference_index":843,"source":"pdf_text","source_observed_at":"2026-08-03T06:26:49.758326Z"},"links":{"cited_paper":"/paper/2403.03950","citing_paper":"/paper/2601.23075"},"observation_digest":"sha256:ec8d24d784cd4cf9f2623ad0b7b30b0a2d3c439abec34f83af4ed3b48d1d7f07","observation_id":"2e14a882-24c4-442f-8c64-ba290c397f12","resolution":{"observed_at":"2026-08-03T06:26:49.758326Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.23150","last_updated":"2025-05-29T06:41:45Z","snapshot_observed_at":"2026-08-09T00:14:25.317749Z","submitted_at":"2025-05-29T06:41:45Z","title":"Bigger, Regularized, Categorical: High-Capacity Value Functions are Efficient Multi-Task Learners","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.23150","snapshot_observed_at":"2026-08-03T06:26:50.060301Z","title":"Mnih, V ., Badia, A","venue":null,"work_id":null,"year":1928},"citing_paper":{"arxiv_id":"2601.23075","last_updated":"2026-06-23T21:44:31Z","snapshot_observed_at":"2026-08-03T06:17:20.457387Z","submitted_at":"2026-01-30T15:24:34Z","title":"RN-D: Discretized Categorical Actors for On-Policy Reinforcement Learning","version":2},"reference_index":1937,"source":"pdf_text","source_observed_at":"2026-08-03T06:26:50.060301Z"},"links":{"cited_paper":"/paper/2505.23150","citing_paper":"/paper/2601.23075"},"observation_digest":"sha256:d96ebcbf09b1c324d240adf0be292b3198f54bbd4156f674e9eb851d38d4d14c","observation_id":"0b865dce-dccd-424b-8539-56b73b144627","resolution":{"observed_at":"2026-08-03T06:26:50.060301Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2503.14476","last_updated":"2025-05-20T01:37:34Z","snapshot_observed_at":"2026-08-02T01:40:54.187278Z","submitted_at":"2025-03-18T17:49:06Z","title":"DAPO: An Open-Source LLM Reinforcement Learning System at Scale","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.14476","snapshot_observed_at":"2026-08-03T06:26:50.223064Z","title":"Williams, R","venue":null,"work_id":null,"year":1992},"citing_paper":{"arxiv_id":"2601.23075","last_updated":"2026-06-23T21:44:31Z","snapshot_observed_at":"2026-08-03T06:17:20.457387Z","submitted_at":"2026-01-30T15:24:34Z","title":"RN-D: Discretized Categorical Actors for On-Policy Reinforcement Learning","version":2},"reference_index":2025,"source":"pdf_text","source_observed_at":"2026-08-03T06:26:50.223064Z"},"links":{"cited_paper":"/paper/2503.14476","citing_paper":"/paper/2601.23075"},"observation_digest":"sha256:7c07c47f9d9a98f6482a1064c7ffaa10fcb01c86b41ba5b7539f1900789248cc","observation_id":"4e0da62e-0158-4e00-bb8b-2e0aa0c9eeb8","resolution":{"observed_at":"2026-08-03T06:26:50.223064Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2601.23075","last_updated":"2026-06-23T21:44:31Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-03T06:17:20.457387Z","submitted_at":"2026-01-30T15:24:34Z","title":"RN-D: Discretized Categorical Actors for On-Policy Reinforcement Learning"},"reference_resolution":{"displayed":6,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":6,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":6},"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":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 6 of 6 outbound references and 1 inbound Pith citation observation for arXiv:2601.23075."}