Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
On the Global Convergence Rates of Softmax Policy Gradient Methods
As of 23 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2005.06392.
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 2026-08-23T06:30:58.430688+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-15T14:39:15.516180Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-16T08:17:36.497807Z
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 c216de61-5a8b-45e0-a88a-3a378f821381 · inbound
Optimal Sample Complexity for Single Time-Scale Actor-Critic with Momentum cites this paper.
Optimal Sample Complexity for Single Time-Scale Actor-Critic with Momentum On the Global Convergence Rates of Softmax Policy Gradient Methods
Reference 39
- Resolution
- verified exact
- arxiv_id, observed 2026-05-16T08:17:36.499390Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 48d7edbf-492b-4f17-ad60-91b93f9125f3 · inbound
Abstention as an Action Can Kill Both the Reward Gradient and the KL Anchor: Collapse Law and Repair for Error-Penalized Reinforcement Learning cites this paper.
Abstention as an Action Can Kill Both the Reward Gradient and the KL Anchor: Collapse Law and Repair for Error-Penalized Reinforcement Learning On the Global Convergence Rates of Softmax Policy Gradient Methods
Reference 16
- Resolution
- unresolved
- no resolver link, observed 2026-08-04T00:53:27.440871Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a2746f34-f148-4e9d-b074-5d3f586fae8e · inbound
Robust Average-Reward Markov Decision Processes: Minimax-Optimal Learning via Plug-in Reductions cites this paper.
Robust Average-Reward Markov Decision Processes: Minimax-Optimal Learning via Plug-in Reductions On the Global Convergence Rates of Softmax Policy Gradient Methods
Reference 60
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T14:39:15.516180Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.