Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
InfoRM: Mitigating Reward Hacking in RLHF via Information-Theoretic Reward Modeling
As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2402.09345.
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 2026-08-19T06:32:44.657259+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-12T00:30:33.400576Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-07T12:35:30.314941Z
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 5ccb960a-0681-47fb-aa64-390ab974365a · inbound
When Can Proxies Improve the Sample Complexity of Preference Learning? cites this paper.
When Can Proxies Improve the Sample Complexity of Preference Learning? InfoRM: Mitigating Reward Hacking in RLHF via Information-Theoretic Reward Modeling
Reference 20
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T10:41:23.993491Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b5eb8da3-2fb5-4a0b-a87f-592c7f958673 · inbound
AMIA: Automatic Masking and Joint Intention Analysis Makes LVLMs Robust Jailbreak Defenders cites this paper.
AMIA: Automatic Masking and Joint Intention Analysis Makes LVLMs Robust Jailbreak Defenders InfoRM: Mitigating Reward Hacking in RLHF via Information-Theoretic Reward Modeling
Reference 19
- Resolution
- verified exact
- local_arxiv, observed 2026-08-07T12:35:30.381649Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation dfafb29f-ee29-4220-a9a3-2eca840c1fcf · inbound
A Unified Framework for Dynamic Reward Shaping in Reinforcement Learning cites this paper.
A Unified Framework for Dynamic Reward Shaping in Reinforcement Learning InfoRM: Mitigating Reward Hacking in RLHF via Information-Theoretic Reward Modeling
Reference 2023
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T00:30:33.400576Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.