Paper Citation Record · LEDGER

Models of human preference for learning reward functions

As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 13 inbound Pith citation observations for arXiv:2206.02231.

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

pith.paper-citation-record.v1
2206.02231 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 13 of 13 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 13 of 13 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T23:58:38.566308Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T11:38:04.667531Z

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 f398cc95-f2a7-411e-910d-76a946347aab · inbound

Solving the Inverse Alignment Problem for Efficient RLHF cites this paper.

Solving the Inverse Alignment Problem for Efficient RLHF Models of human preference for learning reward functions

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-11T15:55:14.429741Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b019230c-ff8d-4849-aafb-1951c269198e · inbound

Policy-labeled Preference Learning: Is Preference Enough for RLHF? cites this paper.

Policy-labeled Preference Learning: Is Preference Enough for RLHF? Models of human preference for learning reward functions

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-15T23:58:38.566308Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 32592992-01cb-4497-a287-6d56e90f44a0 · inbound

DMRL: Data- and Model-aware Reward Learning for Data Extraction cites this paper.

DMRL: Data- and Model-aware Reward Learning for Data Extraction Models of human preference for learning reward functions

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-15T23:39:04.549420Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation baff5cc1-6d0e-4bf8-adc7-3bcd9acfebef · inbound

Reward Models in Deep Reinforcement Learning: A Survey cites this paper.

Reward Models in Deep Reinforcement Learning: A Survey Models of human preference for learning reward functions

Reference 2005

Resolution: unresolved; no resolver link, observed 2026-08-15T19:38:50.656340Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b05217fc-f97d-452d-9eea-6474cbc25f63 · inbound

Misalignment from Treating Means as Ends cites this paper.

Misalignment from Treating Means as Ends Models of human preference for learning reward functions

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-06T17:34:15.199300Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 07537b37-3f91-4156-8081-dfd99ef58de5 · inbound

PrefPalette: Personalized Preference Modeling with Latent Attributes cites this paper.

PrefPalette: Personalized Preference Modeling with Latent Attributes Models of human preference for learning reward functions

Reference 2011

Resolution: unresolved; no resolver link, observed 2026-08-06T16:27:57.235072Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4e95789a-6a4c-468d-bd98-2e455c7d88da · inbound

Mitigating Cognitive Bias in RLHF by Altering Rationality cites this paper.

Mitigating Cognitive Bias in RLHF by Altering Rationality Models of human preference for learning reward functions

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-05-11T04:50:57.056054Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation cc8c8ecb-6ff7-48ba-83b3-8c666bd9948a · inbound

TokenRatio: Principled Token-Level Preference Optimization via Ratio Matching cites this paper.

TokenRatio: Principled Token-Level Preference Optimization via Ratio Matching Models of human preference for learning reward functions

Reference 118

Resolution: verified exact; arxiv_id, observed 2026-05-13T04:57:17.249499Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 1d75e64d-a71d-4dca-bcf1-f1feddab9c6b · inbound

TokenRatio: Principled Token-Level Preference Optimization via Ratio Matching cites this paper.

TokenRatio: Principled Token-Level Preference Optimization via Ratio Matching Models of human preference for learning reward functions

Reference 118

Resolution: verified exact; arxiv_id, observed 2026-05-15T05:45:06.432792Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 45a86250-9081-46fc-b706-3fb36278c0c5 · inbound

Implicit Safety Alignment from Crowd Preferences cites this paper.

Implicit Safety Alignment from Crowd Preferences Models of human preference for learning reward functions

Reference 28

Resolution: verified exact; arxiv_id, observed 2026-05-22T08:31:16.901456Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 4ab85991-56ed-4bba-bfd0-31af34265e63 · inbound

From Imitation to Alignment: Human-Preference Flow Policies for Long-Horizon Sidewalk Navigation cites this paper.

From Imitation to Alignment: Human-Preference Flow Policies for Long-Horizon Sidewalk Navigation Models of human preference for learning reward functions

Reference 39

Resolution: verified exact; arxiv_id, observed 2026-07-03T11:38:04.669002Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation e3b2b4b0-5c97-47fa-a9ab-0c4d8ab12a8b · inbound

LEMUR: Learning to Align with Multi-Objective Reinforcement Learning from Preference Feedback cites this paper.

LEMUR: Learning to Align with Multi-Objective Reinforcement Learning from Preference Feedback Models of human preference for learning reward functions

Reference 250

Resolution: unresolved; no resolver link, observed 2026-08-03T04:39:32.139912Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 34a6412f-ff93-4201-886c-6617e19fa7e0 · inbound

SP3O: Reinforcement Learning from Segment Preferences without Reward Modeling cites this paper.

SP3O: Reinforcement Learning from Segment Preferences without Reward Modeling Models of human preference for learning reward functions

Reference 1952

Resolution: unresolved; no resolver link, observed 2026-08-15T15:02:45.573168Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.