Paper Citation Record · LEDGER

Bayesian Reward Models for LLM Alignment

As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 11 inbound Pith citation observations for arXiv:2402.13210.

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

pith.paper-citation-record.v1
2402.13210 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 11 of 11 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 11 of 11 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T17:41:13.413554Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T00:37:30.066691Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
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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 906ad3da-1dc1-450e-b802-35b45e0d3319 · inbound

Why you don't overfit, and don't need Bayes if you only train for one epoch cites this paper.

Why you don't overfit, and don't need Bayes if you only train for one epoch Bayesian Reward Models for LLM Alignment

Reference 2009

Resolution: unresolved; no resolver link, observed 2026-08-12T17:41:13.413554Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 96f0f89b-53c5-4a16-919a-2d875d3b71d2 · inbound

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

Solving the Inverse Alignment Problem for Efficient RLHF Bayesian Reward Models for LLM Alignment

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-11T15:55:14.495738Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 06152cda-8b23-4307-9751-c981a0d929f3 · inbound

When Can Proxies Improve the Sample Complexity of Preference Learning? cites this paper.

When Can Proxies Improve the Sample Complexity of Preference Learning? Bayesian Reward Models for LLM Alignment

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-11T10:41:24.092589Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 161a171d-292a-4401-bc16-1ec505d985f0 · inbound

Many of Your DPOs are Secretly One: Attempting Unification Through Mutual Information cites this paper.

Many of Your DPOs are Secretly One: Attempting Unification Through Mutual Information Bayesian Reward Models for LLM Alignment

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-10T22:32:56.546850Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 66514782-85be-4daf-b14d-38c05902923e · inbound

Optimising Language Models for Downstream Tasks: A Post-Training Perspective cites this paper.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective Bayesian Reward Models for LLM Alignment

Reference 260

Resolution: unresolved; no resolver link, observed 2026-08-06T22:44:44.755499Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e343fe7e-4f12-44f1-84b3-6cf25f16acf5 · inbound

Aligned Query Expansion: Efficient Query Expansion for Information Retrieval through LLM Alignment cites this paper.

Aligned Query Expansion: Efficient Query Expansion for Information Retrieval through LLM Alignment Bayesian Reward Models for LLM Alignment

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-06T17:24:39.524257Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7cf9db51-0e08-48f4-90c8-a2c8f098b12a · inbound

Mitigating Think-Answer Mismatch in LLM Reasoning Through Noise-Aware Advantage Reweighting cites this paper.

Mitigating Think-Answer Mismatch in LLM Reasoning Through Noise-Aware Advantage Reweighting Bayesian Reward Models for LLM Alignment

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-05T23:10:24.529784Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4440dea5-165d-4523-8693-b37bfd7be1bc · inbound

The Alignment Auditor: A Bayesian Framework for Verifying and Refining LLM Objectives cites this paper.

The Alignment Auditor: A Bayesian Framework for Verifying and Refining LLM Objectives Bayesian Reward Models for LLM Alignment

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-04T11:17:36.758976Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 80a23c92-a12d-4d19-8a64-564e89829535 · inbound

The Hidden Bias of Process Reward Models:PRISM for Rewarding the Right Reasoning cites this paper.

The Hidden Bias of Process Reward Models:PRISM for Rewarding the Right Reasoning Bayesian Reward Models for LLM Alignment

Reference 20

Resolution: verified exact; arxiv_id, observed 2026-07-03T00:37:30.068171Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation fab0b4f4-7813-40ed-972f-2290c1d1aab7 · inbound

BaRA: Bayesian Adaptive Rank Allocation for Parameter-Efficient Fine-Tuning cites this paper.

BaRA: Bayesian Adaptive Rank Allocation for Parameter-Efficient Fine-Tuning Bayesian Reward Models for LLM Alignment

Reference 12

Resolution: metadata mismatch; arxiv_id, observed 2026-06-30T08:04:28.489458Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 388f28e2-382c-41b3-b71c-84ee765bf8a4 · inbound

ConceptSMILE: Auditing the Trustworthiness of Concept-Based Explainable AI cites this paper.

ConceptSMILE: Auditing the Trustworthiness of Concept-Based Explainable AI Bayesian Reward Models for LLM Alignment

Reference 19

Resolution: unresolved; no resolver link, observed 2026-07-13T01:29:02.336552Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.