Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Is RLHF More Difficult than Standard RL?
As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 inbound Pith citation observations for arXiv:2306.14111.
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 2026-08-08T19:40:42.054396Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-04T13:09:51.330383Z
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 a64d54ee-d14d-4af9-a98e-368242dbd36e · inbound
Design Considerations in Offline Preference-based RL cites this paper.
Design Considerations in Offline Preference-based RL Is RLHF More Difficult than Standard RL?
Reference 16
- Resolution
- unresolved
- no resolver link, observed 2026-08-08T19:40:42.054396Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation f0ca88e5-076b-4d1f-974d-5f4c209eb44a · inbound
Outcome-Based Online Reinforcement Learning: Algorithms and Fundamental Limits cites this paper.
Outcome-Based Online Reinforcement Learning: Algorithms and Fundamental Limits Is RLHF More Difficult than Standard RL?
Reference 42
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T14:10:42.886665Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation bc6e284f-0f30-4db7-b4a7-e9324e6af1ae · inbound
Square$\chi$PO: Differentially Private and Robust $\chi^2$-Preference Optimization in Offline Direct Alignment cites this paper.
Square$\chi$PO: Differentially Private and Robust $\chi^2$-Preference Optimization in Offline Direct Alignment Is RLHF More Difficult than Standard RL?
Reference 81
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:45:02.561160Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 68c03123-55da-4bc6-a574-bbf61d4e86f5 · inbound
Sign-SZPO: Provable Preference-based Reinforcement Learning with an Unknown Link Function cites this paper.
Sign-SZPO: Provable Preference-based Reinforcement Learning with an Unknown Link Function Is RLHF More Difficult than Standard RL?
Reference 31
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T11:20:12.854603Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a076c2a1-5fe0-4a7e-ae2c-54027c45ddc9 · inbound
On the optimization dynamics of RLVR: Gradient gap and step size thresholds cites this paper.
On the optimization dynamics of RLVR: Gradient gap and step size thresholds Is RLHF More Difficult than Standard RL?
Reference 23
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T08:36:07.373729Z
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 6befe8be-e0f8-434d-84d1-92d2498a0dd2 · inbound
OPRIDE: Offline Preference-based Reinforcement Learning via In-Dataset Exploration cites this paper.
OPRIDE: Offline Preference-based Reinforcement Learning via In-Dataset Exploration Is RLHF More Difficult than Standard RL?
Reference 47
- Resolution
- verified exact
- arxiv_id, observed 2026-05-15T21:40:20.961449Z
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 52f4111c-04ef-40a4-8821-5420410760d2 · inbound
Convex Optimization for Alignment and Preference Learning on a Single GPU cites this paper.
Convex Optimization for Alignment and Preference Learning on a Single GPU Is RLHF More Difficult than Standard RL?
Reference 46
- Resolution
- verified exact
- arxiv_id, observed 2026-05-25T05:05:23.015101Z
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 5604856b-fb3b-4589-a8f2-4086f6b651d6 · inbound
Online KL-Regularized Reinforcement Learning with Function Approximation under Misspecification cites this paper.
Online KL-Regularized Reinforcement Learning with Function Approximation under Misspecification Is RLHF More Difficult than Standard RL?
Reference 16
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-07-02T12:06:55.886189Z
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 6d598499-4ca3-44e8-90fc-58b233a99fd8 · inbound
Online KL-Regularized Reinforcement Learning with Function Approximation under Misspecification cites this paper.
Online KL-Regularized Reinforcement Learning with Function Approximation under Misspecification Is RLHF More Difficult than Standard RL?
Reference 16
- Resolution
- unresolved
- no resolver link, observed 2026-07-14T18:23:21.126826Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b36b1692-97d3-438d-b55e-ac2f3ea1231f · inbound
Finding Stationary Points by Comparisons cites this paper.
Finding Stationary Points by Comparisons Is RLHF More Difficult than Standard RL?
Reference 45
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-07-04T13:09:51.331914Z
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.