Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Distributionally Robust Model-Based Offline Reinforcement Learning with Near-Optimal Sample Complexity
As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2208.05767.
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 2026-08-07T13:03:54.976956Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-17T01:48:50.988888Z
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 c8cd894a-0807-4521-b6a3-3d3e495534f1 · inbound
Hybrid Cross-domain Robust Reinforcement Learning cites this paper.
Hybrid Cross-domain Robust Reinforcement Learning Distributionally Robust Model-Based Offline Reinforcement Learning with Near-Optimal Sample Complexity
Reference 43
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:03:54.976956Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 723e80d0-6fe5-46e9-aee9-dafd5bfdba2a · inbound
DVPO: Distributional Value Modeling-based Policy Optimization for LLM Post-Training cites this paper.
DVPO: Distributional Value Modeling-based Policy Optimization for LLM Post-Training Distributionally Robust Model-Based Offline Reinforcement Learning with Near-Optimal Sample Complexity
Reference 22
- Resolution
- verified exact
- arxiv_id, observed 2026-05-17T01:48:50.992508Z
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 562ea72f-78db-420d-8cd5-1d8f81f07aea · inbound
Taming the Curses of Multiagency in Robust Markov Games with Large State Space through Linear Function Approximation cites this paper.
Taming the Curses of Multiagency in Robust Markov Games with Large State Space through Linear Function Approximation Distributionally Robust Model-Based Offline Reinforcement Learning with Near-Optimal Sample Complexity
Reference 12
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-09T06:00:35.978178Z
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.