The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
BoNBoN Alignment for Large Language Models and the Sweetness of Best-of-n Sampling
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In the citing paper
“In more general tasks without verification tools, we can conduct techniques like majority vote, self-consistency, and best-of-n to choose the most promising one from candidate responses”
From Rethinking Mixture-of-Agents: Is Mixing Different Large Language Models Beneficial? (2025) · cites this paper as Gui et al., 2024
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Citations
Cites
Xu, H., Sharaf, A., Chen, Y., Tan, W., Shen, L., Van Durme, B., Murray, K., & Kim, Y. J. (2024) · arXiv · doi:10.48550/arxiv.2401.08417 · Cited by 123
RRHF: Rank Responses to Align Language Models with Human Feedback without tears
Yuan, Z., Yuan, H., Tan, C., Wang, W., Huang, S., & Huang, F. (2023) · arXiv · doi:10.48550/arxiv.2304.05302 · Cited by 41
KTO: Model Alignment as Prospect Theoretic Optimization
Ethayarajh, K., Xu, W., Muennighoff, N., Jurafsky, D., & Kiela, D. (2024) · arXiv · doi:10.48550/arxiv.2402.01306 · Cited by 29
Efficient RLHF: Reducing the Memory Usage of PPO
Santacroce, M., Lu, Y., Yu, H., Li, Y., & Shen, Y. (2023) · arXiv · doi:10.48550/arxiv.2309.00754 · Cited by 4
Cited by
Rethinking Mixture-of-Agents: Is Mixing Different Large Language Models Beneficial?
Li, W., Lin, Y., Xia, M., & Jin, C. (2025) · arXiv · doi:10.48550/arxiv.2502.00674 · Cited by 62
BOND: Aligning LLMs with Best-of-N Distillation
Sessa, P. G., Dadashi, R., Hussenot, L., Ferret, J., Vieillard, N., Ramé, A., Shariari, B., Perrin, S., Friesen, A., Cideron, G., et al. (2024) · arXiv · doi:10.48550/arxiv.2407.14622 · Cited by 1
From Harm to Help: Turning Reasoning In-Context Demos into Assets for Reasoning LMs
Wang, H., Liang, W., Fu, Z., Zheng, N., Zhang, Y., Tong, Y., Zhu, T., Jiang, H., Li, C., Wu, J., et al. (2025) · arXiv · doi:10.48550/arxiv.2509.23196
Metadata
- Authors
- Gui, L., Gârbacea, C., & Veitch, V.
- Venue
- Advances in Neural Information Processing Systems 37
- Published
- 2024
- Pages
- 2851-2885
- Cited by
- 4
- References
- 4
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How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
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- CoChat. (2026). Citation check: BoNBoN Alignment for Large Language Models and the Sweetness of Best-of-n Sampling. https://app.cochat.ai/papers/10.52202/079017-0094
- The paper
- Gui, L., Gârbacea, C., & Veitch, V. (2024). BoNBoN Alignment for Large Language Models and the Sweetness of Best-of-n Sampling. Advances in Neural Information Processing Systems 37. https://doi.org/10.52202/079017-0094
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.
