Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Enhancing Vision-Language Model Training with Reinforcement Learning in Synthetic Worlds for Real-World Success
As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2508.04280.
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 2026-08-21T06:32:19.484+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-07-03T17:42:44.309030Z
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
0
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 19e7b882-6f75-4a32-bb56-3598f81b3658 · inbound
RoboAgent: Chaining Basic Capabilities for Embodied Task Planning cites this paper.
RoboAgent: Chaining Basic Capabilities for Embodied Task Planning Enhancing Vision-Language Model Training with Reinforcement Learning in Synthetic Worlds for Real-World Success
Reference 8
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T05:15:57.383961Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 4a08a957-9eef-4af8-af76-05c23cfe80cf · inbound
Towards Generalist Game Players: An Investigation of Foundation Models in the Game Multiverse cites this paper.
Towards Generalist Game Players: An Investigation of Foundation Models in the Game Multiverse Enhancing Vision-Language Model Training with Reinforcement Learning in Synthetic Worlds for Real-World Success
Reference 14
- Resolution
- verified exact
- arxiv_id, observed 2026-05-12T03:26:19.046031Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 84d25fb1-4d0e-44a8-aad1-bbf2cb477c99 · inbound
Towards Generalist Game Players: An Investigation of Foundation Models in the Game Multiverse cites this paper.
Towards Generalist Game Players: An Investigation of Foundation Models in the Game Multiverse Enhancing Vision-Language Model Training with Reinforcement Learning in Synthetic Worlds for Real-World Success
Reference 14
- Resolution
- verified exact
- arxiv_id, observed 2026-05-13T06:47:26.987221Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 85944475-3427-4f92-86fb-ef5f31ca9f26 · inbound
Rank-Then-Act: Reward-Free Control from Frame-Order Progress cites this paper.
Rank-Then-Act: Reward-Free Control from Frame-Order Progress Enhancing Vision-Language Model Training with Reinforcement Learning in Synthetic Worlds for Real-World Success
Reference 2
- Resolution
- verified exact
- arxiv_id, observed 2026-07-03T17:48:45.215393Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.