Paper Citation Record · LEDGER

Principled Penalty-based Methods for Bilevel Reinforcement Learning and RLHF

As of 23 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2402.06886.

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

pith.paper-citation-record.v1
2402.06886 v3

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 2026-08-23T06:30:58.430688+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T11:13:53.668079Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T22:50:34.719612Z

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 3376c172-6c35-4ba7-a8b8-fc4ce14d89f9 · inbound

Unlocking TriLevel Learning with Level-Wise Zeroth Order Constraints: Distributed Algorithms and Provable Non-Asymptotic Convergence cites this paper.

Unlocking TriLevel Learning with Level-Wise Zeroth Order Constraints: Distributed Algorithms and Provable Non-Asymptotic Convergence Principled Penalty-based Methods for Bilevel Reinforcement Learning and RLHF

Reference 114

Resolution: unresolved; no resolver link, observed 2026-08-11T19:10:52.564782Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e128d9c3-b751-4468-8d5a-812b368640e4 · inbound

Game Theory Meets Large Language Models: A Systematic Survey with Taxonomy and New Frontiers cites this paper.

Game Theory Meets Large Language Models: A Systematic Survey with Taxonomy and New Frontiers Principled Penalty-based Methods for Bilevel Reinforcement Learning and RLHF

Reference 178

Resolution: verified exact; local_arxiv, observed 2026-08-07T22:50:34.722851Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 893e5ac8-0439-4968-98f4-9431b38b94b5 · inbound

Learning Explainable Dense Reward Shapes via Bayesian Optimization cites this paper.

Learning Explainable Dense Reward Shapes via Bayesian Optimization Principled Penalty-based Methods for Bilevel Reinforcement Learning and RLHF

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-16T11:13:53.668079Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8c3a598a-c9c9-4410-97d1-b629bb03001e · inbound

Nonconvex Decentralized Stochastic Bilevel Optimization under Heavy-Tailed Noise cites this paper.

Nonconvex Decentralized Stochastic Bilevel Optimization under Heavy-Tailed Noise Principled Penalty-based Methods for Bilevel Reinforcement Learning and RLHF

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-04T16:23:21.764973Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.