Paper Citation Record · LEDGER

Mitigating Reward Over-Optimization in RLHF via Behavior-Supported Regularization

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2503.18130.

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

pith.paper-citation-record.v1
2503.18130 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:01:04.624213Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-05-11T18:36:08.461945Z

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 3d243c16-4172-4782-96f3-490df15f7716 · inbound

Learning a Pessimistic Reward Model in RLHF cites this paper.

Learning a Pessimistic Reward Model in RLHF Mitigating Reward Over-Optimization in RLHF via Behavior-Supported Regularization

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-07T14:01:04.624213Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 19e26f40-c763-4e28-8de7-1b860b6171dd · 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 Mitigating Reward Over-Optimization in RLHF via Behavior-Supported Regularization

Reference 244

Resolution: unresolved; no resolver link, observed 2026-08-06T14:13:07.180434Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d7a9314a-8d64-4a4a-b68a-216badc374aa · inbound

Wasserstein Distributionally Robust Regret Optimization for Reinforcement Learning from Human Feedback cites this paper.

Wasserstein Distributionally Robust Regret Optimization for Reinforcement Learning from Human Feedback Mitigating Reward Over-Optimization in RLHF via Behavior-Supported Regularization

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-05-11T14:46:46.826104Z

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.

Observation d8951a7c-dced-467d-8e24-17feae9cbf5e · inbound

RVPO: Risk-Sensitive Alignment via Variance Regularization cites this paper.

RVPO: Risk-Sensitive Alignment via Variance Regularization Mitigating Reward Over-Optimization in RLHF via Behavior-Supported Regularization

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-05-11T18:36:08.464939Z

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.