Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Learning to Optimize for Reinforcement Learning
As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2302.01470.
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 2026-08-10T06:31:04.303077+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-06T14:48:47.922233Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-06-30T06:54:21.239534Z
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 853ffeb4-20c3-4278-8903-a0cc889d3f0f · inbound
How Should We Meta-Learn Reinforcement Learning Algorithms? cites this paper.
How Should We Meta-Learn Reinforcement Learning Algorithms? Learning to Optimize for Reinforcement Learning
Reference 38
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T14:48:47.922233Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 4b35fd78-c9dd-4303-83a3-40cd843a3605 · inbound
AI Training Manager: Bounded Closed-Loop Control of Adaptive Training Recipes cites this paper.
AI Training Manager: Bounded Closed-Loop Control of Adaptive Training Recipes Learning to Optimize for Reinforcement Learning
Reference 17
- Resolution
- verified exact
- arxiv_id, observed 2026-06-30T06:54:21.241041Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 53d4ecc1-07eb-45a0-9771-76c137642c19 · inbound
ReBRAC-v2: The Return of the King cites this paper.
ReBRAC-v2: The Return of the King Learning to Optimize for Reinforcement Learning
Reference 84
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T00:32:46.754872Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.