# Top-nσ: Not All Logits Are You Need

arXiv: https://arxiv.org/abs/2411.07641 · HTML: https://mlsanity.com/paper/2411.07641/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Nov 2024.

## Built on by (most cited first, 3 of 3)

- [Noise, Adaptation, and Strategy: Assessing LLM Fidelity in Decision-Making](https://mlsanity.com/paper/2508.15926/) (2025)
- [Not All Layers Need Tuning: Selective Layer Restoration Recovers Diversity](https://mlsanity.com/paper/2602.06665/) (2026)
- [LogitsCoder: Towards Efficient Chain-of-Thought Path Search via Logits Preference Decoding for Code Generation](https://mlsanity.com/paper/2602.14054/) (2026)

## Abstract (arXiv)

Large language models (LLMs) typically employ greedy decoding or low-temperature sampling for reasoning tasks, reflecting a perceived trade-off between diversity and accuracy. We challenge this convention by introducing top-nσ, a novel sampling method that operates directly on pre-softmax logits by leveraging a statistical threshold. Our key insight is that logits naturally separate into a Gaussian-distributed noisy region and a distinct informative region, enabling efficient token filtering without complex probability manipulations. Unlike existing methods (e.g., top-p, min-p) that inadvertently include more noise tokens at higher temperatures, top-nσ maintains a stable sampling space regardless of temperature scaling. We also provide a theoretical analysis of top-nσ to better understand its behavior. The extensive experimental results across four reasoning-focused datasets demonstrate that our method not only outperforms existing sampling approaches but also surpasses greedy decoding, while maintaining consistent performance even at high temperatures.