Preference learning made easy: Everything should be understood through win rate | Paper Citation Record

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This paper cites Back to basics: Revisiting reinforce style optimization for learning from human feedback in llms.

Preference learning made easy: Everything should be understood through win rate Back to basics: Revisiting reinforce style optimization for learning from human feedback in llms

Reference 1

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This paper cites Calibration and consistency of adversarial surrogate losses.

Preference learning made easy: Everything should be understood through win rate Calibration and consistency of adversarial surrogate losses

Reference 2

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This paper cites Multi-class h -consistency bounds.

Preference learning made easy: Everything should be understood through win rate Multi-class h -consistency bounds

Reference 3

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This paper cites G., Rowland, M., Piot, B., Guo, D., Calandriello, D., Valko, M., and Munos, R.

Preference learning made easy: Everything should be understood through win rate G., Rowland, M., Piot, B., Guo, D., Calandriello, D., Valko, M., and Munos, R

Reference 4

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

Preference learning made easy: Everything should be understood through win rate Training a helpful and harmless assistant with reinforcement learning from human feedback

Reference 5

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

Preference learning made easy: Everything should be understood through win rate Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling

Reference 6

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This paper cites Quantile Filtered Imitation Learning.

Preference learning made easy: Everything should be understood through win rate Quantile Filtered Imitation Learning

Reference 7

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This paper cites Probabilistic interpretation of feedforward classification network outputs, with relationships to statistical pattern recognition.

Preference learning made easy: Everything should be understood through win rate Probabilistic interpretation of feedforward classification network outputs, with relationships to statistical pattern recognition

Reference 8

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This paper cites Human Alignment of Large Language Models through Online Preference Optimisation.

Preference learning made easy: Everything should be understood through win rate Human Alignment of Large Language Models through Online Preference Optimisation

Reference 9

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This paper cites H., Chen, X., Zhang, Q., Ranganath, R., and Cho, K.

Preference learning made easy: Everything should be understood through win rate H., Chen, X., Zhang, Q., Ranganath, R., and Cho, K

Reference 10

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This paper cites Deep reinforcement learning from human preferences.

Preference learning made easy: Everything should be understood through win rate Deep reinforcement learning from human preferences

Reference 11

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This paper cites Raft: Reward ranked finetuning for generative foundation model alignment.

Preference learning made easy: Everything should be understood through win rate Raft: Reward ranked finetuning for generative foundation model alignment

Reference 12

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This paper cites Understanding dataset difficulty with v-usable information.

Preference learning made easy: Everything should be understood through win rate Understanding dataset difficulty with v-usable information

Reference 13

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This paper cites Bonbon alignment for large language models and the sweetness of best-of-n sampling.

Preference learning made easy: Everything should be understood through win rate Bonbon alignment for large language models and the sweetness of best-of-n sampling

Reference 14

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This paper cites L., Srinivasan, S., Konyushkova, K., Weerts, L., Sharma, A., Siddhant, A., Ahern, A., Wang, M., Gu, C., Macherey, W., Doucet, A., Firat, O., and de Freitas, N.

Preference learning made easy: Everything should be understood through win rate L., Srinivasan, S., Konyushkova, K., Weerts, L., Sharma, A., Siddhant, A., Ahern, A., Wang, M., Gu, C., Macherey, W., Doucet, A., Firat, O., and de Freitas, N

Reference 15

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This paper cites Direct Language Model Alignment from Online AI Feedback.

Preference learning made easy: Everything should be understood through win rate Direct Language Model Alignment from Online AI Feedback

Reference 16

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This paper cites $f$-PO: Generalizing Preference Optimization with $f$-divergence Minimization.

Preference learning made easy: Everything should be understood through win rate $f$-PO: Generalizing Preference Optimization with $f$-divergence Minimization

Reference 17

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

Preference learning made easy: Everything should be understood through win rate Correcting the Mythos of KL-Regularization: Direct Alignment without Overoptimization via Chi-Squared Preference Optimization

Reference 18

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This paper cites A., Choi, Y., and Hajishirzi, H.

Preference learning made easy: Everything should be understood through win rate A., Choi, Y., and Hajishirzi, H

Reference 19

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This paper cites Towards Efficient Exact Optimization of Language Model Alignment.

Preference learning made easy: Everything should be understood through win rate Towards Efficient Exact Optimization of Language Model Alignment

Reference 20

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This paper cites A Survey on Human Preference Learning for Large Language Models.

Preference learning made easy: Everything should be understood through win rate A Survey on Human Preference Learning for Large Language Models

Reference 21

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

Preference learning made easy: Everything should be understood through win rate A survey of reinforcement learning from human feedback

Reference 22

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This paper cites Understanding the effects of rlhf on llm generalisation and diversity.

Preference learning made easy: Everything should be understood through win rate Understanding the effects of rlhf on llm generalisation and diversity

Reference 23

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This paper cites OpenAssistant Conversations -- Democratizing Large Language Model Alignment.

Preference learning made easy: Everything should be understood through win rate OpenAssistant Conversations -- Democratizing Large Language Model Alignment

Reference 24

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This paper cites Huggingface h4 stack exchange preference dataset.

Preference learning made easy: Everything should be understood through win rate Huggingface h4 stack exchange preference dataset

Reference 25

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This paper cites Self-Alignment with Instruction Backtranslation.

Preference learning made easy: Everything should be understood through win rate Self-Alignment with Instruction Backtranslation

Reference 26

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Preference learning made easy: Everything should be understood through win rate Unresolved cited work

Reference 27

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This paper cites Large Language Models: A Survey.

Preference learning made easy: Everything should be understood through win rate Large Language Models: A Survey

Reference 28

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Preference learning made easy: Everything should be understood through win rate Monte carlo gradient estimation in machine learning

Reference 29

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Preference learning made easy: Everything should be understood through win rate Monte carlo gradient estimation in machine learning

Reference 30

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This paper cites G., Rowland, M., Guo, Z.

Preference learning made easy: Everything should be understood through win rate G., Rowland, M., Guo, Z

Reference 31

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Preference learning made easy: Everything should be understood through win rate A., Lindsten, F., and Blei, D

Reference 32

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Preference learning made easy: Everything should be understood through win rate Gpt-4 technical report

Reference 33

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

Preference learning made easy: Everything should be understood through win rate Training language models to follow instructions with human feedback

Reference 34

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Preference learning made easy: Everything should be understood through win rate Smaug: Fixing Failure Modes of Preference Optimisation with DPO-Positive

Reference 35

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This paper cites Scaling Laws for Reward Model Overoptimization in Direct Alignment Algorithms.

Preference learning made easy: Everything should be understood through win rate Scaling Laws for Reward Model Overoptimization in Direct Alignment Algorithms

Reference 36

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Preference learning made easy: Everything should be understood through win rate From r to q^* : Your language model is secretly a q-function

Reference 37

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Preference learning made easy: Everything should be understood through win rate D., Ermon, S., and Finn, C

Reference 38

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This paper cites Black box variational inference.

Preference learning made easy: Everything should be understood through win rate Black box variational inference

Reference 39

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This paper cites Unintentional Unalignment: Likelihood Displacement in Direct Preference Optimization.

Preference learning made easy: Everything should be understood through win rate Unintentional Unalignment: Likelihood Displacement in Direct Preference Optimization

Reference 40

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This paper cites Vanishing gradients in reinforcement finetuning of language models.

Preference learning made easy: Everything should be understood through win rate Vanishing gradients in reinforcement finetuning of language models

Reference 41

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This paper cites Direct nash optimization: Teaching language models to self-improve with general preferences, 2024.

Preference learning made easy: Everything should be understood through win rate Direct nash optimization: Teaching language models to self-improve with general preferences, 2024

Reference 42

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This paper cites Direct Alignment with Heterogeneous Preferences.

Preference learning made easy: Everything should be understood through win rate Direct Alignment with Heterogeneous Preferences

Reference 43

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This paper cites A Long Way to Go: Investigating Length Correlations in RLHF.

Preference learning made easy: Everything should be understood through win rate A Long Way to Go: Investigating Length Correlations in RLHF

Reference 44

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Preference learning made easy: Everything should be understood through win rate Distributional preference learning: Understanding and accounting for hidden context in rlhf

Reference 45

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This paper cites How to compare different loss functions and their risks.

Preference learning made easy: Everything should be understood through win rate How to compare different loss functions and their risks

Reference 46

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Preference learning made easy: Everything should be understood through win rate Learning to summarize from human feedback

Reference 47

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Preference learning made easy: Everything should be understood through win rate S., and Agarwal, A

Reference 48

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This paper cites Preference fine-tuning of llms should leverage suboptimal, on-policy data.

Preference learning made easy: Everything should be understood through win rate Preference fine-tuning of llms should leverage suboptimal, on-policy data

Reference 49

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Preference learning made easy: Everything should be understood through win rate D., Zheng, Z., Calandriello, D., Munos, R., Rowland, M., Richemond, P

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T18:32:22.348188Z

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This paper cites Trl: Transformer reinforcement learning.

Preference learning made easy: Everything should be understood through win rate Trl: Transformer reinforcement learning

Reference 51

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This paper cites Enabling language models to implicitly learn self-improvement.

Preference learning made easy: Everything should be understood through win rate Enabling language models to implicitly learn self-improvement

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T18:32:22.297854Z

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This paper cites Transforming and combining rewards for aligning large language models.

Preference learning made easy: Everything should be understood through win rate Transforming and combining rewards for aligning large language models

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T18:32:22.261958Z

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This paper cites Policy gradient algorithms.

Preference learning made easy: Everything should be understood through win rate Policy gradient algorithms

Reference 54

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This paper cites V., Murray, K., and Kim, Y.

Preference learning made easy: Everything should be understood through win rate V., Murray, K., and Kim, Y

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T18:32:22.201317Z

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 97ad70a4-9085-4d57-ad7e-f3d6738836c3 · outbound

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

Preference learning made easy: Everything should be understood through win rate Some things are more CRINGE than others: Iterative Preference Optimization with the Pairwise Cringe Loss

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-07T18:32:21.300233Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 00f98043-ba0a-4e26-a390-a345ade3e382 · outbound

This paper cites Y., Cho, K., Sukhbaatar, S., Xu, J., and Weston, J.

Preference learning made easy: Everything should be understood through win rate Y., Cho, K., Sukhbaatar, S., Xu, J., and Weston, J

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T18:32:22.175500Z

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 5e8863dd-e6f4-4a5c-8ebe-e6fde31070dd · outbound

This paper cites an unresolved cited work.

Preference learning made easy: Everything should be understood through win rate Unresolved cited work

Reference 58

Resolution: unresolved; raw_fallback, observed 2026-08-07T18:32:22.143704Z

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 862eb67f-a726-4035-9669-b97892d90489 · outbound

This paper cites Judging llm-as-a-judge with mt-bench and chatbot arena.

Preference learning made easy: Everything should be understood through win rate Judging llm-as-a-judge with mt-bench and chatbot arena

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T18:32:22.120972Z

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 e9610168-ebbd-4b38-b0c3-8b7fab8f24bc · outbound

This paper cites I., and Jiao, J.

Preference learning made easy: Everything should be understood through win rate I., and Jiao, J

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T18:32:22.100251Z

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 4419939a-a0f5-49a7-b640-ecc4d607b2e2 · outbound

This paper cites write newline.

Preference learning made easy: Everything should be understood through win rate write newline

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-07T18:32:21.331060Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Coverage vector

Reference resolution

Outbound references

Pith citing papers