Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Efficient Deep Reinforcement Learning Requires Regulating Overfitting
As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2304.10466.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T11:55:35.378696Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-19T17:12:41.377100Z
Reference resolution
0 of 0 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
No outbound reference observations are available for this paper version.
Pith citing papers
Observation 8f19ce19-a2db-4b84-8382-b03ccde148e8 · inbound
Optimistic critics can empower small actors cites this paper.
Optimistic critics can empower small actors Efficient Deep Reinforcement Learning Requires Regulating Overfitting
Reference 26
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T11:55:35.378696Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 06716c5a-e090-4980-9555-a23967eca3b6 · inbound
A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control cites this paper.
A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control Efficient Deep Reinforcement Learning Requires Regulating Overfitting
Reference 23
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T20:31:07.797950Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 38bb763a-9dc1-4e91-be06-8e7d43fffbe8 · inbound
FlashSAC: Fast and Stable Off-Policy Reinforcement Learning for High-Dimensional Robot Control cites this paper.
FlashSAC: Fast and Stable Off-Policy Reinforcement Learning for High-Dimensional Robot Control Efficient Deep Reinforcement Learning Requires Regulating Overfitting
Reference 42
- Resolution
- verified exact
- arxiv_id, observed 2026-05-10T22:15:49.893425Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 58d7c28a-cb1f-4b90-8692-b9b350efc7e0 · inbound
FlashSAC: Fast and Stable Off-Policy Reinforcement Learning for High-Dimensional Robot Control cites this paper.
FlashSAC: Fast and Stable Off-Policy Reinforcement Learning for High-Dimensional Robot Control Efficient Deep Reinforcement Learning Requires Regulating Overfitting
Reference 42
- Resolution
- verified exact
- arxiv_id, observed 2026-05-19T17:12:41.378623Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.