Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
More Efficient Randomized Exploration for Reinforcement Learning via Approximate Sampling
As of 6 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2406.12241.
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 2026-08-05T06:32:48.257954+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-05-21T13:06:54.002248Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-21T13:10:10.486352Z
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 5be92ee5-bb9a-4c30-8eee-26b2f3b9f1ca · inbound
Provably avoiding over-optimization in Direct Preference Optimization without knowing the data distribution cites this paper.
Provably avoiding over-optimization in Direct Preference Optimization without knowing the data distribution More Efficient Randomized Exploration for Reinforcement Learning via Approximate Sampling
Reference 15
- Resolution
- verified exact
- arxiv_id, observed 2026-05-16T06:37:28.592535Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation bf4cb7c3-cffa-45d2-b8db-2c8f1412da16 · inbound
Provably avoiding over-optimization in Direct Preference Optimization without knowing the data distribution cites this paper.
Provably avoiding over-optimization in Direct Preference Optimization without knowing the data distribution More Efficient Randomized Exploration for Reinforcement Learning via Approximate Sampling
Reference 15
- Resolution
- verified exact
- arxiv_id, observed 2026-05-21T13:10:10.488584Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.