Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Rethinking RL Scaling for Vision Language Models: A Transparent, From-Scratch Framework and Comprehensive Evaluation Scheme
As of 16 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2504.02587.
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 2026-08-07T14:31:21.635031Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-15T14:25:55.485325Z
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 249fe37d-913a-4f57-bce9-5e58c6cb992e · inbound
Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models cites this paper.
Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models Rethinking RL Scaling for Vision Language Models: A Transparent, From-Scratch Framework and Comprehensive Evaluation Scheme
Reference 144
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T14:31:21.635031Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 3751446f-e19e-4344-aded-4380b4983e01 · inbound
AdapThink: Adaptive Thinking Preferences for Reasoning Language Model cites this paper.
AdapThink: Adaptive Thinking Preferences for Reasoning Language Model Rethinking RL Scaling for Vision Language Models: A Transparent, From-Scratch Framework and Comprehensive Evaluation Scheme
Reference 11
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T23:28:38.124748Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a00dfc3d-725a-4f49-ba37-4f81fabc7e24 · inbound
StructVRM: Aligning Multimodal Reasoning with Structured and Verifiable Reward Models cites this paper.
StructVRM: Aligning Multimodal Reasoning with Structured and Verifiable Reward Models Rethinking RL Scaling for Vision Language Models: A Transparent, From-Scratch Framework and Comprehensive Evaluation Scheme
Reference 28
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T23:29:16.745968Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation fd64f3fd-3eb2-4b09-b524-f0334f9120d9 · inbound
An Explainable Machine Learning Framework for Railway Predictive Maintenance using Data Streams from the Metro Operator of Portugal cites this paper.
An Explainable Machine Learning Framework for Railway Predictive Maintenance using Data Streams from the Metro Operator of Portugal Rethinking RL Scaling for Vision Language Models: A Transparent, From-Scratch Framework and Comprehensive Evaluation Scheme
Reference 28
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T23:25:49.866310Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 0b743c39-d685-4595-8830-73d73e5953c1 · inbound
CODA: Difficulty-Aware Compute Allocation for Adaptive Reasoning cites this paper.
CODA: Difficulty-Aware Compute Allocation for Adaptive Reasoning Rethinking RL Scaling for Vision Language Models: A Transparent, From-Scratch Framework and Comprehensive Evaluation Scheme
Reference 22
- Resolution
- verified exact
- arxiv_id, observed 2026-05-15T14:25:55.487214Z
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.