Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
OpenRFT: Adapting Reasoning Foundation Model for Domain-specific Tasks with Reinforcement Fine-Tuning
As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2412.16849.
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 2026-08-09T06:31:02.800959+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-08T20:51:16.752470Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-22T21:42:10.897720Z
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 a94e1cca-6cea-46b3-bd8e-c45649e4fd76 · inbound
Dynamic Chain-of-Thought: Towards Adaptive Deep Reasoning cites this paper.
Dynamic Chain-of-Thought: Towards Adaptive Deep Reasoning OpenRFT: Adapting Reasoning Foundation Model for Domain-specific Tasks with Reinforcement Fine-Tuning
Reference 22
- Resolution
- unresolved
- no resolver link, observed 2026-08-08T20:51:16.752470Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation eb61f1dc-8ae3-46a6-8d7e-9793e55ffc54 · inbound
Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systems cites this paper.
Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systems OpenRFT: Adapting Reasoning Foundation Model for Domain-specific Tasks with Reinforcement Fine-Tuning
Reference 162
- Resolution
- verified exact
- arxiv_id, observed 2026-05-22T21:42:10.900414Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 68bec4cf-22ee-441b-8a54-330e0d72bbc7 · inbound
Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library cites this paper.
Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library OpenRFT: Adapting Reasoning Foundation Model for Domain-specific Tasks with Reinforcement Fine-Tuning
Reference 57
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T06:02:33.060236Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 1d01e0c7-cdfc-4bd7-84b9-761b9432e179 · inbound
One Token to Fool LLM-as-a-Judge cites this paper.
One Token to Fool LLM-as-a-Judge OpenRFT: Adapting Reasoning Foundation Model for Domain-specific Tasks with Reinforcement Fine-Tuning
Reference 41
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T18:15:40.104909Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation af82a7c5-9336-4f8c-a046-38fcc0f9ade4 · inbound
Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models cites this paper.
Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models OpenRFT: Adapting Reasoning Foundation Model for Domain-specific Tasks with Reinforcement Fine-Tuning
Reference 58
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T20:21:08.255765Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.