Paper Citation Record · LEDGER

Self-Consistency of the Internal Reward Models Improves Self-Rewarding Language Models

As of 14 August 2026, this Paper Citation Record lists 49 of 49 outbound references and 4 inbound Pith citation observations for arXiv:2502.08922.

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

pith.paper-citation-record.v1
2502.08922 v1

Coverage vector

measured 49 of 49 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T23:18:40.394477Z

measured 53 of 53 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:09:46.031769Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-13T17:08:01.250732Z

Reference resolution

49 of 49 outbound references displayed

verified exact1
verified fuzzy13
unresolved35
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External citation measurements

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Outbound references

Observation eb139baf-7580-4c82-8155-cc86f5db0d28 · outbound

This paper cites Meta-rewarding language models: Self-improving alignment with LLM -as-a-meta-judge.

Self-Consistency of the Internal Reward Models Improves Self-Rewarding Language Models Meta-rewarding language models: Self-improving alignment with LLM -as-a-meta-judge

Reference 1

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Observation e724814b-ff4b-49ce-bd8d-18587919ea51 · outbound

This paper cites Training a helpful and harmless assistant with reinforcement learning from human feedback, 2022 a.

Self-Consistency of the Internal Reward Models Improves Self-Rewarding Language Models Training a helpful and harmless assistant with reinforcement learning from human feedback, 2022 a

Reference 2

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Observation 9e892fca-77c4-4940-9743-4061a4d54fa9 · outbound

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

Self-Consistency of the Internal Reward Models Improves Self-Rewarding Language Models Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback

Reference 3

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Observation 88a07b6c-c796-4124-84bb-16bfdcd50f94 · outbound

This paper cites E., Fort, S., Lanham, T., Telleen-Lawton, T., Conerly, T., Henighan, T., Hume, T., Bowman, S.

Self-Consistency of the Internal Reward Models Improves Self-Rewarding Language Models E., Fort, S., Lanham, T., Telleen-Lawton, T., Conerly, T., Henighan, T., Hume, T., Bowman, S

Reference 4

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Observation 7b5a1aec-238c-4517-8551-cdacf8658b67 · outbound

This paper cites On the Opportunities and Risks of Foundation Models.

Self-Consistency of the Internal Reward Models Improves Self-Rewarding Language Models On the Opportunities and Risks of Foundation Models

Reference 5

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Observation 8421daa7-8b6a-424e-8e6b-1658d8da8220 · outbound

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Self-Consistency of the Internal Reward Models Improves Self-Rewarding Language Models Unresolved cited work

Reference 6

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Observation f0305702-5474-4972-9d5e-3d4203c67e02 · outbound

This paper cites an unresolved cited work.

Self-Consistency of the Internal Reward Models Improves Self-Rewarding Language Models Unresolved cited work

Reference 7

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This paper cites H., Baker, B., Gao, L., Aschenbrenner, L., Chen, Y., Ecoffet, A., Joglekar, M., Leike, J., et al.

Self-Consistency of the Internal Reward Models Improves Self-Rewarding Language Models H., Baker, B., Gao, L., Aschenbrenner, L., Chen, Y., Ecoffet, A., Joglekar, M., Leike, J., et al

Reference 8

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Observation 2c615e10-6313-4219-b04a-95e09e6e31af · outbound

This paper cites Discovering latent knowledge in language models without supervision.

Self-Consistency of the Internal Reward Models Improves Self-Rewarding Language Models Discovering latent knowledge in language models without supervision

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:18:41.554612Z

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Observation d46c78dd-3243-433c-9bfb-621697db17cf · outbound

This paper cites Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak Supervision.

Self-Consistency of the Internal Reward Models Improves Self-Rewarding Language Models Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak Supervision

Reference 10

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Observation 1163456b-9200-41ef-9695-08a2fd6e6d56 · outbound

This paper cites Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback.

Self-Consistency of the Internal Reward Models Improves Self-Rewarding Language Models Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

Reference 12

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Self-Consistency of the Internal Reward Models Improves Self-Rewarding Language Models Unresolved cited work

Reference 13

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Observation c7fafe0c-56c6-451f-b9cd-cf2034c80ae6 · outbound

This paper cites Training verifiers to solve math word problems, 2021.

Self-Consistency of the Internal Reward Models Improves Self-Rewarding Language Models Training verifiers to solve math word problems, 2021

Reference 14

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Observation b6f2cf57-9420-4492-867a-7d51ea2b01a1 · outbound

This paper cites MetaRM: Shifted Distributions Alignment via Meta-Learning.

Self-Consistency of the Internal Reward Models Improves Self-Rewarding Language Models MetaRM: Shifted Distributions Alignment via Meta-Learning

Reference 15

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Observation a435c9e9-de7c-40ac-ac1c-dbf3d4c2c9f1 · outbound

This paper cites Length-Controlled AlpacaEval: A Simple Way to Debias Automatic Evaluators.

Self-Consistency of the Internal Reward Models Improves Self-Rewarding Language Models Length-Controlled AlpacaEval: A Simple Way to Debias Automatic Evaluators

Reference 16

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Observation 0faf2016-1197-42bf-9496-d41fc3b97446 · outbound

This paper cites and Bengio, Y.

Self-Consistency of the Internal Reward Models Improves Self-Rewarding Language Models and Bengio, Y

Reference 17

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Observation 81736b11-9a44-490a-b19a-5d4fe0d9ae56 · outbound

This paper cites The Llama 3 Herd of Models.

Self-Consistency of the Internal Reward Models Improves Self-Rewarding Language Models The Llama 3 Herd of Models

Reference 18

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Observation d4e7d5dd-168e-4055-9f3a-aca257409d27 · outbound

This paper cites A Survey on LLM-as-a-Judge.

Self-Consistency of the Internal Reward Models Improves Self-Rewarding Language Models A Survey on LLM-as-a-Judge

Reference 20

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This paper cites Measuring massive multitask language understanding.

Self-Consistency of the Internal Reward Models Improves Self-Rewarding Language Models Measuring massive multitask language understanding

Reference 21

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Observation c61ef4f5-603d-4696-bfcf-d7a32ee4e1da · outbound

This paper cites Large Language Models Can Self-Improve.

Self-Consistency of the Internal Reward Models Improves Self-Rewarding Language Models Large Language Models Can Self-Improve

Reference 22

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Observation 89abcfe5-4980-4830-a70f-a54d6375978a · outbound

This paper cites Instruct2Act: Mapping Multi-modality Instructions to Robotic Actions with Large Language Model.

Self-Consistency of the Internal Reward Models Improves Self-Rewarding Language Models Instruct2Act: Mapping Multi-modality Instructions to Robotic Actions with Large Language Model

Reference 23

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Observation fa350cd6-20a9-4804-aa81-23c4452eae54 · outbound

This paper cites Mistral 7B.

Self-Consistency of the Internal Reward Models Improves Self-Rewarding Language Models Mistral 7B

Reference 24

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Observation 4af70f34-2ea6-4aab-8e0f-5eda5533175c · outbound

This paper cites A survey of reinforcement learning from human feedback, 2024.

Self-Consistency of the Internal Reward Models Improves Self-Rewarding Language Models A survey of reinforcement learning from human feedback, 2024

Reference 25

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Self-Consistency of the Internal Reward Models Improves Self-Rewarding Language Models o pf, A., Kilcher, Y., von R \

Reference 26

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Observation c9581455-2476-41a8-b899-d9ccae36168b · outbound

This paper cites RewardBench: Evaluating Reward Models for Language Modeling.

Self-Consistency of the Internal Reward Models Improves Self-Rewarding Language Models RewardBench: Evaluating Reward Models for Language Modeling

Reference 27

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Observation 0cfd3253-0bdc-4c0a-accf-e50797e06f7a · outbound

This paper cites Skywork-Reward: Bag of Tricks for Reward Modeling in LLMs.

Self-Consistency of the Internal Reward Models Improves Self-Rewarding Language Models Skywork-Reward: Bag of Tricks for Reward Modeling in LLMs

Reference 28

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Self-Consistency of the Internal Reward Models Improves Self-Rewarding Language Models and Hutter, F

Reference 29

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Self-Consistency of the Internal Reward Models Improves Self-Rewarding Language Models BioMedGPT: Open Multimodal Generative Pre-trained Transformer for BioMedicine

Reference 30

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Self-Consistency of the Internal Reward Models Improves Self-Rewarding Language Models Introducing ChatGPT

Reference 31

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This paper cites Training language models to follow instructions with human feedback.

Self-Consistency of the Internal Reward Models Improves Self-Rewarding Language Models Training language models to follow instructions with human feedback

Reference 32

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Self-Consistency of the Internal Reward Models Improves Self-Rewarding Language Models Iterative Reasoning Preference Optimization

Reference 34

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Self-Consistency of the Internal Reward Models Improves Self-Rewarding Language Models Disentangling length from quality in direct preference optimization

Reference 35

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Self-Consistency of the Internal Reward Models Improves Self-Rewarding Language Models D., Ermon, S., and Finn, C

Reference 36

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This paper cites Proximal Policy Optimization Algorithms.

Self-Consistency of the Internal Reward Models Improves Self-Rewarding Language Models Proximal Policy Optimization Algorithms

Reference 37

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This paper cites Loose lips sink ships: Mitigating length bias in reinforcement learning from human feedback.

Self-Consistency of the Internal Reward Models Improves Self-Rewarding Language Models Loose lips sink ships: Mitigating length bias in reinforcement learning from human feedback

Reference 38

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Observation 7f7c52d3-b3d6-435b-9a49-90fde801f122 · outbound

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Self-Consistency of the Internal Reward Models Improves Self-Rewarding Language Models A Long Way to Go: Investigating Length Correlations in RLHF

Reference 39

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Self-Consistency of the Internal Reward Models Improves Self-Rewarding Language Models Unresolved cited work

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Self-Consistency of the Internal Reward Models Improves Self-Rewarding Language Models Llama: Open and efficient foundation language models, 2023

Reference 41

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Self-Consistency of the Internal Reward Models Improves Self-Rewarding Language Models Aligning Large Language Models with Human: A Survey

Reference 42

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This paper cites CREAM: Consistency Regularized Self-Rewarding Language Models.

Self-Consistency of the Internal Reward Models Improves Self-Rewarding Language Models CREAM: Consistency Regularized Self-Rewarding Language Models

Reference 43

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This paper cites Meta-Rewarding Language Models: Self-Improving Alignment with LLM-as-a-Meta-Judge.

Self-Consistency of the Internal Reward Models Improves Self-Rewarding Language Models Meta-Rewarding Language Models: Self-Improving Alignment with LLM-as-a-Meta-Judge

Reference 44

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Self-Consistency of the Internal Reward Models Improves Self-Rewarding Language Models Unsupervised data augmentation for consistency training

Reference 45

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This paper cites Some things are more CRINGE than others: Iterative Preference Optimization with the Pairwise Cringe Loss.

Self-Consistency of the Internal Reward Models Improves Self-Rewarding Language Models Some things are more CRINGE than others: Iterative Preference Optimization with the Pairwise Cringe Loss

Reference 46

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Self-Consistency of the Internal Reward Models Improves Self-Rewarding Language Models Y., Cho, K., Li, X., Sukhbaatar, S., Xu, J., and Weston, J

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:18:41.257658Z

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Observation 99baab46-c283-4db2-bea8-20b2703cc36e · outbound

This paper cites Consistency regularization for cross-lingual fine-tuning.

Self-Consistency of the Internal Reward Models Improves Self-Rewarding Language Models Consistency regularization for cross-lingual fine-tuning

Reference 48

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Observation e33d1246-26e6-4c54-b606-ade210b624df · outbound

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Self-Consistency of the Internal Reward Models Improves Self-Rewarding Language Models E., and Stoica, I

Reference 49

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This paper cites Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena.

Self-Consistency of the Internal Reward Models Improves Self-Rewarding Language Models Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena

Reference 50

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This paper cites Lima: Less is more for alignment, 2023.

Self-Consistency of the Internal Reward Models Improves Self-Rewarding Language Models Lima: Less is more for alignment, 2023

Reference 51

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Self-Consistency of the Internal Reward Models Improves Self-Rewarding Language Models write newline

Reference 52

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Pith citing papers

Observation 7a523748-4ad1-4f59-b1a4-e6c6a8b30bc4 · inbound

From System 1 to System 2: A Survey of Reasoning Large Language Models cites this paper.

From System 1 to System 2: A Survey of Reasoning Large Language Models Self-Consistency of the Internal Reward Models Improves Self-Rewarding Language Models

Reference 171

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Observation 97b1f075-2d3f-4a02-bc76-f2266f53f3a7 · inbound

Consistent Paths Lead to Truth: Self-Rewarding Reinforcement Learning for LLM Reasoning cites this paper.

Consistent Paths Lead to Truth: Self-Rewarding Reinforcement Learning for LLM Reasoning Self-Consistency of the Internal Reward Models Improves Self-Rewarding Language Models

Reference 63

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Observation 18db4afd-80b6-4da9-b0f4-c8e5b09750d9 · inbound

Temporal Self-Rewarding Language Models: Decoupling Chosen-Rejected via Past-Future cites this paper.

Temporal Self-Rewarding Language Models: Decoupling Chosen-Rejected via Past-Future Self-Consistency of the Internal Reward Models Improves Self-Rewarding Language Models

Reference 43

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Observation 9b43603d-a844-4c6f-9943-96b546c31597 · inbound

Can LLMs Learn to Reason Robustly under Noisy Supervision? cites this paper.

Can LLMs Learn to Reason Robustly under Noisy Supervision? Self-Consistency of the Internal Reward Models Improves Self-Rewarding Language Models

Reference 36

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