Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
A General Framework for Sample-Efficient Function Approximation in Reinforcement Learning
As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2209.15634.
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 2026-08-07T04:54:02.731207Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-10T12:55:24.132930Z
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 ce103053-a1c5-4338-9e01-e38b1ffa5044 · inbound
The Sample Complexity of Online Strategic Decision Making with Information Asymmetry and Knowledge Transportability cites this paper.
The Sample Complexity of Online Strategic Decision Making with Information Asymmetry and Knowledge Transportability A General Framework for Sample-Efficient Function Approximation in Reinforcement Learning
Reference 9
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T04:54:02.731207Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b8d695b5-c96e-4e3b-818a-5995305f991b · inbound
Provably Efficient Offline-to-Online Value Adaptation with General Function Approximation cites this paper.
Provably Efficient Offline-to-Online Value Adaptation with General Function Approximation A General Framework for Sample-Efficient Function Approximation in Reinforcement Learning
Reference 1
- Resolution
- verified exact
- arxiv_id, observed 2026-05-10T12:55:24.135226Z
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.