Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Countering Reward Over-optimization in LLM with Demonstration-Guided Reinforcement Learning
As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2404.19409.
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 2026-08-08T06:32:00.761636+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T05:10:47.735478Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-11T12:46:05.148870Z
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 71434e16-9a23-4888-9167-faa6a70701b9 · inbound
MEMETRON: Metaheuristic Mechanisms for Test-time Response Optimization of Large Language Models cites this paper.
MEMETRON: Metaheuristic Mechanisms for Test-time Response Optimization of Large Language Models Countering Reward Over-optimization in LLM with Demonstration-Guided Reinforcement Learning
Reference 31
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T05:10:47.735478Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b595ddec-1e1b-43db-a522-d74a07f306cf · inbound
EAPO: Enhancing Policy Optimization with On-Demand Expert Assistance cites this paper.
EAPO: Enhancing Policy Optimization with On-Demand Expert Assistance Countering Reward Over-optimization in LLM with Demonstration-Guided Reinforcement Learning
Reference 2020
- Resolution
- unresolved
- no resolver link, observed 2026-08-04T14:44:14.954740Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 1412861e-0ced-4a33-adbb-e5877f1a6925 · inbound
Pause or Fabricate? Training Language Models for Grounded Reasoning cites this paper.
Pause or Fabricate? Training Language Models for Grounded Reasoning Countering Reward Over-optimization in LLM with Demonstration-Guided Reinforcement Learning
Reference 29
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T12:46:05.154606Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.