Paper Citation Record · LEDGER

Optimal Design for Reward Modeling in RLHF

As of 4 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2410.17055.

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

pith.paper-citation-record.v1
2410.17055 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-04T06:34:03.388597+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T09:12:22.082616Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T01:09:19.289406Z

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 c3994666-13af-4383-80f3-607b41a01877 · inbound

Eliciting Truthful Feedback for Preference-Based Learning via the VCG Mechanism cites this paper.

Eliciting Truthful Feedback for Preference-Based Learning via the VCG Mechanism Optimal Design for Reward Modeling in RLHF

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-04T09:12:22.082616Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4192b025-d1c3-4f6d-8276-58b6253b1bc3 · inbound

Reinforcement Learning from Human Feedback: A Statistical Perspective cites this paper.

Reinforcement Learning from Human Feedback: A Statistical Perspective Optimal Design for Reward Modeling in RLHF

Reference 69

Resolution: verified exact; arxiv_id, observed 2026-05-13T20:13:13.666333Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 6e9067c7-4567-4bfe-ae86-6977d6cac3c2 · inbound

Goal-Conditioned Supervised Learning for LLM Fine-Tuning cites this paper.

Goal-Conditioned Supervised Learning for LLM Fine-Tuning Optimal Design for Reward Modeling in RLHF

Reference 1

Resolution: verified exact; arxiv_id, observed 2026-05-20T22:39:10.107245Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation eb3e82c5-b9af-46c4-9e5c-052b03094655 · inbound

How Neural Reward Models Learn Features for Policy Optimization: A Single-Index Analysis cites this paper.

How Neural Reward Models Learn Features for Policy Optimization: A Single-Index Analysis Optimal Design for Reward Modeling in RLHF

Reference 27

Resolution: metadata mismatch; arxiv_id, observed 2026-06-30T12:04:38.765753Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation efda342b-a451-4167-9d3d-ef5f417e3c6b · inbound

Which Pairs to Compare for LLM Post-Training? cites this paper.

Which Pairs to Compare for LLM Post-Training? Optimal Design for Reward Modeling in RLHF

Reference 28

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T01:09:19.293176Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.