LLM reasoning / chain-of-thought
Best-of-N
Superseded — cited as a baseline and beaten by newer methods
3 papers critique it · 4 beat it on benchmarks
What papers say
Verbatim critique sentences, each from a paper that cites Best-of-N as a baseline.
the far simpler method of majority voting wang2023selfconsistency, which completely ignores the expensive PRM and relies solely on the consensus of the LLM's own generations, can outperform PRM-guided BoN.
“restricting PRM evaluation to complete CoTs misses opportunities for dynamic guidance”
“In all of these methods the guidance signal---critique, scoring model, abstraction prompt---is produced by an untrained, prompted LLM.”
Beaten on benchmarks
Head-to-head results where a newer method reports beating Best-of-N. Values are copied from the source paper's tables — verify against the cited paper.
BoN+IAS w/ Calib. PRM beats Best-of-N
0.2342 vs 1.0
Budget Ratio · [MATH500 / Qwen-2.5-7B]
Know What You Don't Know: Uncertainty Calibration of Process Reward ModelsSequential beats Best-of-N
0.916 vs 0.573
Bias · [GPT-4o-mini, English]
Decoding-Time Debiasing via Process Reward Models: From Controlled Fill-in to Open-Ended GenerationCo-ReAct beats Best-of-N
36.92 vs 33.19
DeepResearchBench Average · [Qwen3-14B]
Co-ReAct: Rubrics as Step-Level Collaborators for ReAct AgentsLogit WV beats Best-of-N
57.6 vs 52.7
Average · [Qwen-PRM800K-7B]
Optimal Aggregation of LLM and PRM Signals for Efficient Test-Time Scaling
What to use instead
Recent methods in the same sub-problem, not yet superseded in the knowledge base — arXiv benchmark leaders, not vetted production recommendations.
- May 27, 2026
- Tree-of-ThoughtsTree of Thoughts as a Classical Heuristic Search Problem: Formal Foundations and Design PatternsMay 27, 2026
- May 22, 2026
- May 22, 2026
- Novelty-based Tree-of-Thought SearchNovelty-based Tree-of-Thought Search for LLM Reasoning and PlanningMay 7, 2026
- Decoding-Time Debiasing via Process Reward ModelsDecoding-Time Debiasing via Process Reward Models: From Controlled Fill-in to Open-Ended GenerationMay 4, 2026
- Apr 27, 2026
- Apr 22, 2026
- CoT-PoT ensemblingSelf-Consistency from Only Two Samples: CoT-PoT Ensembling for Efficient LLM ReasoningApr 19, 2026
- AtroposAtropos: Improving Cost-Benefit Trade-off of LLM-based Agents under Self-Consistency with Early Termination and Model HotswapApr 16, 2026
- Apr 1, 2026
- Learning When to SampleLearning When to Sample: Confidence-Aware Self-Consistency for Efficient LLM Chain-of-Thought ReasoningMar 17, 2026