Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Hybrid Reinforcement Learning Breaks Sample Size Barriers in Linear MDPs
As of 16 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2408.04526.
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 2026-08-15T06:32:42.880941+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-15T20:21:29.691317Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-11T15:55:20.561430Z
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 8e75d090-37fc-4dae-b31b-168ccefad6f8 · inbound
Hybrid Preference Optimization for Alignment: Provably Faster Convergence Rates by Combining Offline Preferences with Online Exploration cites this paper.
Hybrid Preference Optimization for Alignment: Provably Faster Convergence Rates by Combining Offline Preferences with Online Exploration Hybrid Reinforcement Learning Breaks Sample Size Barriers in Linear MDPs
Reference 18
- Resolution
- verified exact
- local_arxiv, observed 2026-08-11T15:55:20.567068Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 55fb2b55-bc45-4ca8-842e-fe744e94b712 · inbound
Augmenting Online RL with Offline Data is All You Need: A Unified Hybrid RL Algorithm Design and Analysis cites this paper.
Augmenting Online RL with Offline Data is All You Need: A Unified Hybrid RL Algorithm Design and Analysis Hybrid Reinforcement Learning Breaks Sample Size Barriers in Linear MDPs
Reference 55
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T20:21:29.691317Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.