Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Unlock the Correlation between Supervised Fine-Tuning and Reinforcement Learning in Training Code Large Language Models
As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2406.10305.
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 2026-08-10T06:31:04.303077+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-06-27T01:13:11.483599Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-03T20:38:56.065163Z
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 c25c2c08-16a5-4711-81b7-9ff75a054061 · inbound
AutoOR: Scalably Post-training LLMs to Autoformalize Operations Research Problems cites this paper.
AutoOR: Scalably Post-training LLMs to Autoformalize Operations Research Problems Unlock the Correlation between Supervised Fine-Tuning and Reinforcement Learning in Training Code Large Language Models
Reference 68
- Resolution
- verified exact
- arxiv_id, observed 2026-05-10T07:11:53.641585Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation a1f3be3a-d229-4aca-bc40-8d18874dd6a3 · inbound
From Reasoning Traces to Reusable Modules: Understanding Compositional Generalization in Language Model Reasoning cites this paper.
From Reasoning Traces to Reusable Modules: Understanding Compositional Generalization in Language Model Reasoning Unlock the Correlation between Supervised Fine-Tuning and Reinforcement Learning in Training Code Large Language Models
Reference 142
- Resolution
- verified exact
- arxiv_id, observed 2026-07-03T20:38:56.066602Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.