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Paper Citation Record · LEDGER

Loose lips sink ships: Mitigating Length Bias in Reinforcement Learning from Human Feedback

As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2310.05199.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2310.05199 v5

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 8 of 8 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T22:51:52.565038Z

measured 1 of 1 external citation measurements

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 961c987f-8b53-498f-825c-d25e44eeab2a · inbound

An Overview and Discussion on Using Large Language Models for Implementation Generation of Solutions to Open-Ended Problems cites this paper.

An Overview and Discussion on Using Large Language Models for Implementation Generation of Solutions to Open-Ended Problems Loose lips sink ships: Mitigating Length Bias in Reinforcement Learning from Human Feedback

Reference 109

Resolution
unresolved
no resolver link, observed 2026-08-10T22:51:52.565038Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T22:51:52.565038Z digest=sha256:da554000b805b23b20baf488e6bddd606b4697b5d9710d0e0df88fbf80d6a7ae

Observation 4f3b8895-aa70-41ab-a6f6-33d222789660 · inbound

Beyond Reward Hacking: Causal Rewards for Large Language Model Alignment cites this paper.

Beyond Reward Hacking: Causal Rewards for Large Language Model Alignment Loose lips sink ships: Mitigating Length Bias in Reinforcement Learning from Human Feedback

Reference 55

Resolution
unresolved
no resolver link, observed 2026-08-10T19:55:50.742656Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T19:55:50.742656Z digest=sha256:980b8873e7c50b29f22129512804264b7e11c689998bed1f966fa2f82a9898b3

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.

source=arxiv_source observed=2026-08-09T17:46:29.264777Z digest=sha256:f7c5bdbdad7480f609df837185854997e332043a5ff5c25ce66d42c52bd42011

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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-05-19T09:40:58.067398Z digest=sha256:be61a5a8e557d0983b4ba926b8fa526f2259bf228fd8dc88ab4ed772a3fbdb89

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 2026-08-14T06:32:32.682623+00:00.

source=arxiv_source observed=2026-05-19T05:16:22.274580Z digest=sha256:6ad4f87e7ede6099927b4fda610f84580b1f8422d90ef2154e2ea087a3b0b8c1

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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-05-21T23:24:43.556606Z digest=sha256:48b8f866a3cb7564ae83e4f1e622d1cc890a3dd8c000c425ae1e3f7e298ab601

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.

source=arxiv_source observed=2026-08-06T14:13:07.183552Z digest=sha256:15f72027a43523f4b8839d2d918cd003b63a90c9ea7a5423549d13b198d16e6b

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 2026-08-14T06:32:32.682623+00:00.

source=arxiv_source observed=2026-06-27T17:26:17.072017Z digest=sha256:3b63a3845a2b662af1428eb7c2b5b2b1c8c16b689c450a344a6ea9234b2323a4