Paper Citation Record · LEDGER

Learning a Pessimistic Reward Model in RLHF

As of 8 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 3 inbound Pith citation observations for arXiv:2505.20556.

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

pith.paper-citation-record.v1
2505.20556 v1

Coverage vector

measured 31 of 31 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:01:07.462011Z

measured 34 of 34 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-27T17:11:46.549150Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

31 of 31 outbound references displayed

verified exact1
verified fuzzy3
unresolved27
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

0
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation de6fb29f-26b0-426e-92ab-19cb013c0f0c · outbound

This paper cites [2024b], with probability at least 1 − δ, the last term can be bound by V µ r∗ (π) − V µ r∗ (ˆπ) ≤ CµD (R, π, πref)2 8κ2 · β + 3β N log( Nϵ(R, ∥ · ∥∞) δ ).

Learning a Pessimistic Reward Model in RLHF [2024b], with probability at least 1 − δ, the last term can be bound by V µ r∗ (π) − V µ r∗ (ˆπ) ≤ CµD (R, π, πref)2 8κ2 · β + 3β N log( Nϵ(R, ∥ · ∥∞) δ )

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:01:09.146820Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 36e8ddd2-9c26-4b78-93aa-403379b37e37 · outbound

This paper cites The rejection sampling process is effectively a policy as it takes a prompt as input and stochastically outputs a response.

Learning a Pessimistic Reward Model in RLHF The rejection sampling process is effectively a policy as it takes a prompt as input and stochastically outputs a response

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:01:08.680829Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 85ccfc5b-4c63-4600-9e91-6f20604f8e6a · outbound

This paper cites Helping or Herding? Reward Model Ensembles Mitigate but do not Eliminate Reward Hacking.

Learning a Pessimistic Reward Model in RLHF Helping or Herding? Reward Model Ensembles Mitigate but do not Eliminate Reward Hacking

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T14:01:04.764200Z

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Unavailable: canonical work link unavailable.

Observation f499fe4f-e977-411a-88ef-e517db30b68f · outbound

This paper cites Robust Preference Optimization through Reward Model Distillation.

Learning a Pessimistic Reward Model in RLHF Robust Preference Optimization through Reward Model Distillation

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T14:01:04.861147Z

Source-reported events for the cited work

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Observation 1a636902-5b51-4f76-86a8-8078c4290c71 · outbound

This paper cites Mitigating Preference Hacking in Policy Optimization with Pessimism.

Learning a Pessimistic Reward Model in RLHF Mitigating Preference Hacking in Policy Optimization with Pessimism

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-07T14:01:04.976843Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6c726fd7-51c0-4dcb-858c-88becab87c76 · outbound

This paper cites Correcting the Mythos of KL-Regularization: Direct Alignment without Overoptimization via Chi-Squared Preference Optimization.

Learning a Pessimistic Reward Model in RLHF Correcting the Mythos of KL-Regularization: Direct Alignment without Overoptimization via Chi-Squared Preference Optimization

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T14:01:05.069235Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 362722c1-ba46-4730-a3e0-bd411823339a · outbound

This paper cites The N+ Implementation Details of RLHF with PPO: A Case Study on TL;DR Summarization.

Learning a Pessimistic Reward Model in RLHF The N+ Implementation Details of RLHF with PPO: A Case Study on TL;DR Summarization

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T14:01:05.147815Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 846f9a8e-399f-4bad-bf6b-ea1336025286 · outbound

This paper cites RS-DPO: A Hybrid Rejection Sampling and Direct Preference Optimization Method for Alignment of Large Language Models.

Learning a Pessimistic Reward Model in RLHF RS-DPO: A Hybrid Rejection Sampling and Direct Preference Optimization Method for Alignment of Large Language Models

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T14:01:05.235610Z

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Observation cc13c926-2db6-4395-8878-9f430c3aedc4 · outbound

This paper cites Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems.

Learning a Pessimistic Reward Model in RLHF Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T14:01:05.329676Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 10740686-15de-458a-8a08-82ef54fc1c73 · outbound

This paper cites Statistical Rejection Sampling Improves Preference Optimization.

Learning a Pessimistic Reward Model in RLHF Statistical Rejection Sampling Improves Preference Optimization

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T14:01:05.396920Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3e72c6b5-9edd-4f59-b902-d1674575148e · outbound

This paper cites RRM: Robust Reward Model Training Mitigates Reward Hacking.

Learning a Pessimistic Reward Model in RLHF RRM: Robust Reward Model Training Mitigates Reward Hacking

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-07T14:01:05.490143Z

Source-reported events for the cited work

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Observation a2b21913-dd8a-401e-bf9e-3fe44b655998 · outbound

This paper cites WARM: On the Benefits of Weight Averaged Reward Models.

Learning a Pessimistic Reward Model in RLHF WARM: On the Benefits of Weight Averaged Reward Models

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T14:01:05.649860Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cf583308-af18-4836-b38e-fa24ff026969 · outbound

This paper cites Direct Nash Optimization: Teaching Language Models to Self-Improve with General Preferences.

Learning a Pessimistic Reward Model in RLHF Direct Nash Optimization: Teaching Language Models to Self-Improve with General Preferences

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-07T14:01:05.734257Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dd3eca1a-614b-4892-a228-e5e9e4d1e819 · outbound

This paper cites Proximal Policy Optimization Algorithms.

Learning a Pessimistic Reward Model in RLHF Proximal Policy Optimization Algorithms

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T14:01:05.832817Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 752f47ed-e83d-4753-81d5-9c3d5d35c94f · outbound

This paper cites Boosting Reward Model with Preference-Conditional Multi-Aspect Synthetic Data Generation.

Learning a Pessimistic Reward Model in RLHF Boosting Reward Model with Preference-Conditional Multi-Aspect Synthetic Data Generation

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-07T14:01:05.949991Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 400d8bdf-8572-4ceb-ba4e-f027564c672b · outbound

This paper cites Generalized Preference Optimization: A Unified Approach to Offline Alignment.

Learning a Pessimistic Reward Model in RLHF Generalized Preference Optimization: A Unified Approach to Offline Alignment

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-07T14:01:06.110662Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2188078f-3ec4-47cd-8dcd-da45e6a778b8 · outbound

This paper cites Causal Confusion and Reward Misidentification in Preference-Based Reward Learning.

Learning a Pessimistic Reward Model in RLHF Causal Confusion and Reward Misidentification in Preference-Based Reward Learning

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-07T14:01:06.259098Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation efd341ea-140e-4c02-b775-92ebc86fb584 · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

Learning a Pessimistic Reward Model in RLHF LLaMA: Open and Efficient Foundation Language Models

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-07T14:01:06.403374Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7defe75f-a49b-407f-84a5-9bff0f301c07 · outbound

This paper cites Making RL with Preference-based Feedback Efficient via Randomization.

Learning a Pessimistic Reward Model in RLHF Making RL with Preference-based Feedback Efficient via Randomization

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-07T14:01:06.516230Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f7e99903-dfd7-4e45-a6ac-6fd3782b22ad · outbound

This paper cites Exploratory Preference Optimization: Harnessing Implicit Q*-Approximation for Sample-Efficient RLHF.

Learning a Pessimistic Reward Model in RLHF Exploratory Preference Optimization: Harnessing Implicit Q*-Approximation for Sample-Efficient RLHF

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-07T14:01:06.669955Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c925f773-402e-4005-aad0-6c67f4ba00c6 · outbound

This paper cites Iterative Preference Learning from Human Feedback: Bridging Theory and Practice for RLHF under KL-Constraint.

Learning a Pessimistic Reward Model in RLHF Iterative Preference Learning from Human Feedback: Bridging Theory and Practice for RLHF under KL-Constraint

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-07T14:01:06.783481Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a3074639-539a-49a7-97b8-496c5ef4f467 · outbound

This paper cites Provable Offline Preference-Based Reinforcement Learning.

Learning a Pessimistic Reward Model in RLHF Provable Offline Preference-Based Reinforcement Learning

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-07T14:01:06.936809Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9a3474c4-bb56-4ffb-89d4-bf52cc09386e · outbound

This paper cites Self-Exploring Language Models: Active Preference Elicitation for Online Alignment.

Learning a Pessimistic Reward Model in RLHF Self-Exploring Language Models: Active Preference Elicitation for Online Alignment

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-07T14:01:07.063483Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 43c8e79b-099b-42df-b0df-f7e4b8b3a09b · outbound

This paper cites Logarithmic regret for online kl-regularized reinforcement learning.

Learning a Pessimistic Reward Model in RLHF Logarithmic regret for online kl-regularized reinforcement learning

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-07T14:01:07.182899Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 192b52ba-3723-4214-ab5e-f95fd3bc5dcf · outbound

This paper cites 1 will increase the bound on the performance gap analysis, which is undesired for sampling efficiency.

Learning a Pessimistic Reward Model in RLHF 1 will increase the bound on the performance gap analysis, which is undesired for sampling efficiency

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:01:08.907969Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation df8a4b7b-f1ed-41fc-98b0-148f8d11c3fb · outbound

This paper cites Value-Incentivized Preference Optimization: A Unified Approach to Online and Offline RLHF.

Learning a Pessimistic Reward Model in RLHF Value-Incentivized Preference Optimization: A Unified Approach to Online and Offline RLHF

Reference 1952

Resolution: unresolved; no resolver link, observed 2026-08-07T14:01:04.563716Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3d243c16-4172-4782-96f3-490df15f7716 · outbound

This paper cites Mitigating Reward Over-Optimization in RLHF via Behavior-Supported Regularization.

Learning a Pessimistic Reward Model in RLHF Mitigating Reward Over-Optimization in RLHF via Behavior-Supported Regularization

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-07T14:01:04.624213Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 301e7a58-e6e9-434e-9935-0ccf2266b1bf · outbound

This paper cites Sample-Efficient Reinforcement Learning from Human Feedback via Information-Directed Sampling.

Learning a Pessimistic Reward Model in RLHF Sample-Efficient Reinforcement Learning from Human Feedback via Information-Directed Sampling

Reference 2022

Resolution: verified exact; local_arxiv, observed 2026-08-07T14:01:08.191682Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 44fed6cc-8e4a-4b34-b7fe-75eaa37a3fbd · outbound

This paper cites Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling.

Learning a Pessimistic Reward Model in RLHF Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-07T14:01:04.472540Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8a6d0d9c-1030-42b3-add9-adf75ffc89b3 · outbound

This paper cites Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback.

Learning a Pessimistic Reward Model in RLHF Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-07T14:01:04.407989Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 81bbaf25-9f91-4f8c-a513-b6862e9d21ba · outbound

This paper cites RLHF Workflow: From Reward Modeling to Online RLHF.

Learning a Pessimistic Reward Model in RLHF RLHF Workflow: From Reward Modeling to Online RLHF

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-07T14:01:04.698570Z

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Unavailable: canonical work link unavailable.

Pith citing papers

Observation b4b2d56e-a2c4-49c5-af8c-331bc75722da · inbound

Wasserstein Distributionally Robust Regret Optimization for Reinforcement Learning from Human Feedback cites this paper.

Wasserstein Distributionally Robust Regret Optimization for Reinforcement Learning from Human Feedback Learning a Pessimistic Reward Model in RLHF

Reference 19

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T14:46:47.349064Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 3eb09b5e-b6d2-4617-b84c-8e79bd1ac3ba · inbound

Benchmarking and Improving Monitors for Out-Of-Distribution Alignment Failure in LLMs cites this paper.

Benchmarking and Improving Monitors for Out-Of-Distribution Alignment Failure in LLMs Learning a Pessimistic Reward Model in RLHF

Reference 73

Resolution: verified exact; arxiv_id, observed 2026-05-22T09:41:21.460204Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation bfa36826-0ee0-4a04-925f-e4fd9f11aaef · inbound

A Unifying Lens on Reward Uncertainty in RLHF cites this paper.

A Unifying Lens on Reward Uncertainty in RLHF Learning a Pessimistic Reward Model in RLHF

Reference 18

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T00:27:29.825574Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.