Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Loose lips sink ships: Mitigating Length Bias in Reinforcement Learning from Human Feedback
As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2310.05199.
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 2026-08-09T17:46:29.264777Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z
Reference resolution
0 of 0 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
0
arxiv_reference, observed 2026-08-05T02:28:24.338817Z
Outbound references
No outbound reference observations are available for this paper version.
Pith citing papers
Observation 28f7d9d3-3b58-4538-a665-abd9caa2bc19 · inbound
Disentangling Length Bias In Preference Learning Via Response-Conditioned Modeling cites this paper.
Disentangling Length Bias In Preference Learning Via Response-Conditioned Modeling Loose lips sink ships: Mitigating Length Bias in Reinforcement Learning from Human Feedback
Reference 41
- Resolution
- unresolved
- no resolver link, observed 2026-08-09T17:46:29.264777Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c3771d21-524a-44ae-9b10-f077f7913be5 · inbound
Exploring the Secondary Risks of Large Language Models cites this paper.
Exploring the Secondary Risks of Large Language Models Loose lips sink ships: Mitigating Length Bias in Reinforcement Learning from Human Feedback
Reference 40
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-19T09:42:13.951127Z
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 951dbe1d-40b7-4a48-a20e-a6444fa4a072 · inbound
Teach a Reward Model to Correct Itself: Reward Guided Adversarial Failure Discovery for Robust Reward Modeling cites this paper.
Teach a Reward Model to Correct Itself: Reward Guided Adversarial Failure Discovery for Robust Reward Modeling Loose lips sink ships: Mitigating Length Bias in Reinforcement Learning from Human Feedback
Reference 29
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-19T05:17:05.907121Z
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 46083a4f-feed-45d9-bf47-7d29457ad3d1 · inbound
CoLD: Counterfactually-Guided Length Debiasing for Process Reward Models in Mathematical Reasoning cites this paper.
CoLD: Counterfactually-Guided Length Debiasing for Process Reward Models in Mathematical Reasoning Loose lips sink ships: Mitigating Length Bias in Reinforcement Learning from Human Feedback
Reference 18
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-21T23:25:45.328850Z
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 ae0956be-9318-4a88-b0ec-4ec60946cfd0 · inbound
Alignment and Safety in Large Language Models: Safety Mechanisms, Training Paradigms, and Emerging Challenges cites this paper.
Alignment and Safety in Large Language Models: Safety Mechanisms, Training Paradigms, and Emerging Challenges Loose lips sink ships: Mitigating Length Bias in Reinforcement Learning from Human Feedback
Reference 245
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T14:13:07.183552Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 527946fd-ff7d-4ec1-82a9-b937f441ce71 · inbound
DynaCF: Mitigating Shortcut Learning in Reward Models via Dynamic Counterfactual Sensitivity cites this paper.
DynaCF: Mitigating Shortcut Learning in Reward Models via Dynamic Counterfactual Sensitivity Loose lips sink ships: Mitigating Length Bias in Reinforcement Learning from Human Feedback
Reference 29
- Resolution
- verified exact
- arxiv_id, observed 2026-06-27T17:31:06.976718Z
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.