Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Supervised Pretraining Can Learn In-Context Reinforcement Learning
As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2306.14892.
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 2026-08-06T06:34:29.942622+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-06T15:30:40.975135Z
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
1
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 f4e02161-42d8-43f8-9165-6ef40fa7e2bd · inbound
Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolution cites this paper.
Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolution Supervised Pretraining Can Learn In-Context Reinforcement Learning
Reference 194
- Resolution
- verified exact
- arxiv_id, observed 2026-05-16T08:12:35.080068Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation b808f807-603a-42fb-bcf0-4b2f612a05ff · inbound
Interaction as Intelligence: Deep Research With Human-AI Partnership cites this paper.
Interaction as Intelligence: Deep Research With Human-AI Partnership Supervised Pretraining Can Learn In-Context Reinforcement Learning
Reference 45
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T15:30:40.975135Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 51a9fedf-71d2-4fba-a256-aea2cab9baa1 · inbound
One for All: A Non-Linear Transformer can Enable Cross-Domain Generalization for In-Context Reinforcement Learning cites this paper.
One for All: A Non-Linear Transformer can Enable Cross-Domain Generalization for In-Context Reinforcement Learning Supervised Pretraining Can Learn In-Context Reinforcement Learning
Reference 9
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-12T06:56:31.935629Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 0d394af4-8626-4273-aa97-f8663abe2f41 · inbound
TokenRatio: Principled Token-Level Preference Optimization via Ratio Matching cites this paper.
TokenRatio: Principled Token-Level Preference Optimization via Ratio Matching Supervised Pretraining Can Learn In-Context Reinforcement Learning
Reference 82
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-13T04:57:17.371685Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation c55a68a0-d79c-4972-838b-c8e9b2fef293 · inbound
TokenRatio: Principled Token-Level Preference Optimization via Ratio Matching cites this paper.
TokenRatio: Principled Token-Level Preference Optimization via Ratio Matching Supervised Pretraining Can Learn In-Context Reinforcement Learning
Reference 82
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-15T05:45:06.497953Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 3a18d0c7-fd70-4d9a-9dd7-2e0936c7274d · inbound
Reinforcement Learning Foundation Models Should Already Be A Thing cites this paper.
Reinforcement Learning Foundation Models Should Already Be A Thing Supervised Pretraining Can Learn In-Context Reinforcement Learning
Reference 11
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-07-03T23:39:05.055344Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation e7d950d4-a278-414c-ae1e-03b695a73b5c · inbound
Towards Scalable Multi-Task Reinforcement Learning with Large Decision Models cites this paper.
Towards Scalable Multi-Task Reinforcement Learning with Large Decision Models Supervised Pretraining Can Learn In-Context Reinforcement Learning
Reference 7
- Resolution
- verified exact
- arxiv_id, observed 2026-07-04T16:29:57.124700Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.