Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Policy Mirror Descent for Regularized Reinforcement Learning: A Generalized Framework with Linear Convergence
As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2105.11066.
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 2026-08-05T12:37:42.367138Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z
Reference resolution
0 of 0 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
8
arxiv_reference, observed 2026-08-05T02:28:24.338817Z
Outbound references
No outbound reference observations are available for this paper version.
Pith citing papers
Observation 0b3b05ba-0956-46ea-af58-317195781842 · inbound
The Geometry of Nonlinear Reinforcement Learning cites this paper.
The Geometry of Nonlinear Reinforcement Learning Policy Mirror Descent for Regularized Reinforcement Learning: A Generalized Framework with Linear Convergence
Reference 20
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T12:37:42.367138Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 8dce0f95-36d7-4dc9-82ef-34e0cdb87a4c · inbound
Reference-Sampled Boltzmann Projection for KL-Regularized RLVR: Target-Matched Weighted SFT, Finite One-Shot Gaps, and Policy Mirror Descent cites this paper.
Reference-Sampled Boltzmann Projection for KL-Regularized RLVR: Target-Matched Weighted SFT, Finite One-Shot Gaps, and Policy Mirror Descent Policy Mirror Descent for Regularized Reinforcement Learning: A Generalized Framework with Linear Convergence
Reference 55
- Resolution
- verified exact
- arxiv_id, observed 2026-05-09T05:45:23.160241Z
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 199e1e8e-768d-454b-85c3-f9478d3a9671 · inbound
Priced Motion Through Optimal Faces: A Normal-Fan Geometry for Non-Stationary Adversarial MDPs cites this paper.
Priced Motion Through Optimal Faces: A Normal-Fan Geometry for Non-Stationary Adversarial MDPs Policy Mirror Descent for Regularized Reinforcement Learning: A Generalized Framework with Linear Convergence
Reference 38
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-06-30T09:24:31.621153Z
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.