--- title: "Who cites BOND: Aligning LLMs with Best-of-N Distillation" description: "22 papers of the Elamur Research library cite «BOND: Aligning LLMs with Best-of-N Distillation», found in their bibliographies; the most cited first." canonical: "https://research.elamur.ai/free/en/papers/sessa-2024-bond-aligning-llms-best-n/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/sessa-2024-bond-aligning-llms-best-n/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [BOND: Aligning LLMs with Best-of-N Distillation](https://md.research.elamur.ai/free/en/papers/sessa-2024-bond-aligning-llms-best-n/) / cited by # Who cites «BOND: Aligning LLMs with Best-of-N Distillation» 22 papers of our library cite it (of the 149,439 whose bibliographies we read) · 14 with a page here, the most cited first · [← back to «BOND: Aligning LLMs with Best-of-N Distillation»](https://md.research.elamur.ai/free/en/papers/sessa-2024-bond-aligning-llms-best-n/) 1. [**A Survey on Test-Time Scaling in Large Language Models: What, How, Where, and How Well? arXiv:2503.24235**Qiyuan Zhang, Fuyuan Lyu, Zexu Sun, et al.*2025 · 79 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2025-survey-test-time-scaling-large/) 2. [**Scaling Large Language Model-based Multi-Agent Collaboration arXiv:2406.07155**Chen Qian, Zihao Xie, YiFei Wang, et al.*2024 · 70 citations*](https://md.research.elamur.ai/free/en/papers/qian-2024-scaling-large-language-model/) 3. **[Fast Best-of-N Decoding via Speculative Rejection](https://md.research.elamur.ai/free/en/papers/sun-2024-fast-best-n-decoding-speculative/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Hanshi Sun, Momin Haider, Ruiqi Zhang, et al.*2024 · 50 citations* 4. [**A Survey of Frontiers in LLM Reasoning: Inference Scaling, Learning to Reason, and Agentic Systems arXiv:2504.09037**Zixuan Ke, Fangkai Jiao, Yifei Ming, et al.*2025 · 39 citations*](https://md.research.elamur.ai/free/en/papers/ke-2025-survey-frontiers-llm-reasoning-inference/) 5. **[Revisiting the Test-Time Scaling of o1-like Models: Do they Truly Possess Test-Time Scaling Capabilities?](https://md.research.elamur.ai/free/en/papers/zeng-2025-revisiting-test-time-scaling-o1/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zhiyuan Zeng, Qinyuan Cheng, Zhangyue Yin, et al.*2025 · 38 citations* 6. [**Scaling of Search and Learning: A Roadmap to Reproduce o1 from Reinforcement Learning Perspective arXiv:2412.14135**Zhiyuan Zeng, Qinyuan Cheng, Zhangyue Yin, et al.*2024 · 33 citations*](https://md.research.elamur.ai/free/en/papers/zeng-2024-scaling-search-learning-roadmap-reproduce/) 7. [**RMB: Comprehensively Benchmarking Reward Models in LLM Alignment arXiv:2410.09893**Enyu Zhou, Guodong Zheng, Binghai Wang, et al.*2024 · 30 citations*](https://md.research.elamur.ai/free/en/papers/zhou-2024-rmb-comprehensively-benchmarking-reward-models/) 8. [**Self-Improvement in Language Models: The Sharpening Mechanism arXiv:2412.01951**Audrey Huang; Adam Block; Dylan J. Foster; Dhruv Rohatgi; Cyril Zhang; Max Simchowitz; Jordan T. Ash;, Akshay Krishnamurthy*2024 · 20 citations*](https://md.research.elamur.ai/free/en/papers/huang-2024-self-improvement-language-models-sharpening/) 9. [**Exploring Data Scaling Trends and Effects in Reinforcement Learning from Human Feedback arXiv:2503.22230**Wei Shen, Guanlin Liu, Zheng Wu, et al.*2025 · 12 citations*](https://md.research.elamur.ai/free/en/papers/shen-2025-exploring-data-scaling-trends-effects/) 10. **[AlphaOne: Reasoning Models Thinking Slow and Fast at Test Time](https://md.research.elamur.ai/free/en/papers/zhang-2025-alphaone-reasoning-models-thinking-slow/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Junyu Zhang, Runpei Dong, Han Wang, et al.*2025 · 10 citations* 11. [**Weight Ensembling Improves Reasoning in Language Models arXiv:2504.10478 · COLM 2025**Xingyu Dang, Christina Baek, Kaiyue Wen, et al.*2025 · 10 citations*](https://md.research.elamur.ai/free/en/papers/dang-2025-weight-ensembling-improves-reasoning-language/) 12. **[Rethinking Fine-Tuning when Scaling Test-Time Compute: Limiting Confidence Improves Mathematical Reasoning](https://md.research.elamur.ai/free/en/papers/chen-2025-rethinking-fine-tuning-scaling-test/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Feng Chen, Allan Raventos, Nan Cheng, et al.*2025 · 9 citations* 13. **[Bias Fitting to Mitigate Length Bias of Reward Model in RLHF](https://md.research.elamur.ai/free/en/papers/zhao-2026-bias-fitting-mitigate-length-bias/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Kangwen Zhao, Jianfeng Cai, Jinhua Zhu, et al.*2026 · 2 citations* 14. **[Calibrating Inference Time Alignment with Sequence-level Risk Accumulation](https://md.research.elamur.ai/free/en/papers/tan-2026-calibrating-inference-time-alignment-sequence/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Shanwen Tan, Ziyang Dong, Wei Ju, et al.*2026* 8 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/sessa-2024-bond-aligning-llms-best-n/) — BOND: Aligning LLMs with Best-of-N Distillation