The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Personalizing Reinforcement Learning from Human Feedback with Variational Preference Learning
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Claims other papers make about this one
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In the citing paper
“There are some other emerging personalized alignment studies beyond the”
From A survey of personalized large language models: progress and future directions (2025) · cites this paper as Poddar et al., 2024
In the citing paper
“Inferring user-specific latent distributions from a few preference annotations enables more accurate and personalized reward modeling with better data efficiency.”
From A survey of personalized large language models: progress and future directions (2025) · cites this paper as Poddar et al., 2024
In the citing paper
“VPL [Poddar et al., 2024] utilizes a variational inference framework to capture diverse human preferences through user-specific latent variables.”
From A survey of personalized large language models: progress and future directions (2025) · cites this paper as Poddar et al., 2024
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Citations
Cites
Rank Analysis of Incomplete Block Designs: I. The Method of Paired Comparisons
Bradley, R. A., & Terry, M. E. (1952) · Biometrika · doi:10.2307/2334029 · Cited by 2,412
An algorithmic perspective on imitation learning
Osa, T., Pajarinen, J., Neumann, G., Bagnell, J. A., Abbeel, P., & Peters, J. (2018) · Foundations and Trends in Robotics · doi:10.1561/2300000053 · Cited by 405
Scalable agent alignment via reward modeling: a research direction
Leike, J., Krueger, D., Everitt, T., Martic, M., Maini, V., & Legg, S. (2018) · arXiv · doi:10.48550/arxiv.1811.07871 · Cited by 121
Cited by
A survey of personalized large language models: progress and future directions
Liu, J., Qiu, Z., Li, Z., Dai, Q., Yu, W., Zhu, J., Hu, M., Yang, M., Chua, T.-S., & King, I. (2025) · arXiv · doi:10.48550/arxiv.2502.11528 · Cited by 146
Metadata
- Authors
- Poddar, S., Wan, Y., Ivison, H., Gupta, A., & Jaques, N.
- Venue
- Advances in Neural Information Processing Systems 37
- Published
- 2024
- Pages
- 52516-52544
- Cited by
- 15
- References
- 3
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
