Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Overcoming Reward Overoptimization via Adversarial Policy Optimization with Lightweight Uncertainty Estimation
As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2403.05171.
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 2026-08-09T20:34:01.363519Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-01T21:06:13.284428Z
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 90e9075d-5f38-4246-9229-ddb5551d8801 · inbound
The Energy Loss Phenomenon in RLHF: A New Perspective on Mitigating Reward Hacking cites this paper.
The Energy Loss Phenomenon in RLHF: A New Perspective on Mitigating Reward Hacking Overcoming Reward Overoptimization via Adversarial Policy Optimization with Lightweight Uncertainty Estimation
Reference 39
- Resolution
- unresolved
- no resolver link, observed 2026-08-09T20:34:01.363519Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a9dd4566-6569-4195-97c2-5c64f7de45c9 · inbound
Reviving The Classics: Active Reward Modeling in Large Language Model Alignment cites this paper.
Reviving The Classics: Active Reward Modeling in Large Language Model Alignment Overcoming Reward Overoptimization via Adversarial Policy Optimization with Lightweight Uncertainty Estimation
Reference 46
- Resolution
- unresolved
- no resolver link, observed 2026-08-09T11:47:17.656530Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 0c6ff2cd-dc93-4461-8ad5-5cef5b5228ba · inbound
Bradley-Terry and Multi-Objective Reward Modeling Are Complementary cites this paper.
Bradley-Terry and Multi-Objective Reward Modeling Are Complementary Overcoming Reward Overoptimization via Adversarial Policy Optimization with Lightweight Uncertainty Estimation
Reference 31
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T18:51:28.518998Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d3396fdf-285c-4f06-9ab9-8ce1b7348ab2 · inbound
Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities cites this paper.
Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities Overcoming Reward Overoptimization via Adversarial Policy Optimization with Lightweight Uncertainty Estimation
Reference 114
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T16:34:25.291921Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b7a53fee-808b-4f5c-ac37-e2dea604045c · inbound
Efficient Exploration for Iterative Nash Preference Optimization cites this paper.
Efficient Exploration for Iterative Nash Preference Optimization Overcoming Reward Overoptimization via Adversarial Policy Optimization with Lightweight Uncertainty Estimation
Reference 86
- Resolution
- verified exact
- arxiv_id, observed 2026-07-01T21:06:13.286270Z
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.