Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Never Stop Learning: The Effectiveness of Fine-Tuning in Robotic Reinforcement Learning
As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2004.10190.
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 2026-08-16T04:34:34.268065Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-04T08:29:41.688986Z
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 3655163f-0bf5-429b-8b3a-382f9c8e6832 · inbound
Transferable Adversarial Attacks on Black-Box Vision-Language Models cites this paper.
Transferable Adversarial Attacks on Black-Box Vision-Language Models Never Stop Learning: The Effectiveness of Fine-Tuning in Robotic Reinforcement Learning
Reference 31
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T04:34:34.268065Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 64091f06-2072-49fe-9128-d1612f70ffca · inbound
VLA-RL: Towards Masterful and General Robotic Manipulation with Scalable Reinforcement Learning cites this paper.
VLA-RL: Towards Masterful and General Robotic Manipulation with Scalable Reinforcement Learning Never Stop Learning: The Effectiveness of Fine-Tuning in Robotic Reinforcement Learning
Reference 34
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-16T12:55:40.420276Z
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 e0548e26-4b01-4c05-ac3c-ca7de706ca46 · inbound
Multimodal Fusion for Sim2real Transfer in Visual Reinforcement Learning cites this paper.
Multimodal Fusion for Sim2real Transfer in Visual Reinforcement Learning Never Stop Learning: The Effectiveness of Fine-Tuning in Robotic Reinforcement Learning
Reference 7
- Resolution
- verified exact
- arxiv_id, observed 2026-05-22T00:00:47.815454Z
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 1f3898ce-1aa2-4b76-be26-c866f3fe296f · inbound
Zero-shot Transfer of Reinforcement Learning Control Policies for the Swing-Up and Stabilization of a Cart-Pole System cites this paper.
Zero-shot Transfer of Reinforcement Learning Control Policies for the Swing-Up and Stabilization of a Cart-Pole System Never Stop Learning: The Effectiveness of Fine-Tuning in Robotic Reinforcement Learning
Reference 32
- Resolution
- malformed identifier
- arxiv_id, observed 2026-07-04T08:29:41.690497Z
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.