Cover Image

Min-p, Max Exaggeration: A Critical Analysis of Min-p Sampling in Language Models

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Schaeffer, Rylan, Kazdan, Joshua, Denisov-Blanch, Yegor
Format:	Preprint
Published:	2025
Subjects:	Computation and Language Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Consensus is Not Verification: Why Crowd Wisdom Strategies Fail for LLM Truthfulness
by: Denisov-Blanch, Yegor, et al.
Published: (2026)

Position: Machine Learning Conferences Should Establish a "Refutations and Critiques" Track
by: Schaeffer, Rylan, et al.
Published: (2025)

Position: Model Collapse Does Not Mean What You Think
by: Schaeffer, Rylan, et al.
Published: (2025)

MaxMin-RLHF: Alignment with Diverse Human Preferences
by: Chakraborty, Souradip, et al.
Published: (2024)

MinMax Recurrent Neural Cascades
by: Ronca, Alessandro
Published: (2026)

Quantifying the Effect of Test Set Contamination on Generative Evaluations
by: Schaeffer, Rylan, et al.
Published: (2026)

MinMaxMin $Q$-learning
by: Soffair, Nitsan, et al.
Published: (2024)

Min-K%++: Improved Baseline for Detecting Pre-Training Data from Large Language Models
by: Zhang, Jingyang, et al.
Published: (2024)

A Biased Estimator for MinMax Sampling and Distributed Aggregation
by: Wolfrath, Joel, et al.
Published: (2024)

A mean teacher algorithm for unlearning of language models
by: Klochkov, Yegor
Published: (2025)

VisMin: Visual Minimal-Change Understanding
by: Awal, Rabiul, et al.
Published: (2024)

Min-$k$ Sampling: Decoupling Truncation from Temperature Scaling via Relative Logit Dynamics
by: Ding, Yuanhao, et al.
Published: (2026)

Understanding Adversarial Transfer: Why Representation-Space Attacks Fail Where Data-Space Attacks Succeed
by: Gupta, Isha, et al.
Published: (2025)

Efficient Prediction of Pass@k Scaling in Large Language Models
by: Kazdan, Joshua, et al.
Published: (2025)

Investigating Data Contamination for Pre-training Language Models
by: Jiang, Minhao, et al.
Published: (2024)

Continuous-Time Analysis of Heavy Ball Momentum in Min-Max Games
by: Feng, Yi, et al.
Published: (2025)

On the Convergence of Min-Max Langevin Dynamics and Algorithm
by: Cai, Yang, et al.
Published: (2024)

Revisiting Min-Max Optimization Problem in Adversarial Training
by: Ahmadi, Sina Hajer, et al.
Published: (2024)

Bandit Max-Min Fair Allocation
by: Harada, Tsubasa, et al.
Published: (2025)

Decentralized Personalized Federated Learning for Min-Max Problems
by: Borodich, Ekaterina, et al.
Published: (2021)

Second-Order Min-Max Optimization with Lazy Hessians
by: Chen, Lesi, et al.
Published: (2024)

Smooth Min-Max Monotonic Networks
by: Igel, Christian
Published: (2023)

Retain-Neutral Surrogates for Min-Max Unlearning
by: Cai, Junhao, et al.
Published: (2026)

Turning Up the Heat: Min-p Sampling for Creative and Coherent LLM Outputs
by: Nguyen, Minh Nhat, et al.
Published: (2024)

Solving Neural Min-Max Games: The Role of Architecture, Initialization & Dynamics
by: Patel, Deep, et al.
Published: (2025)

Min-Max Optimization Requires Exponentially Many Queries
by: Bernasconi, Martino, et al.
Published: (2026)

Beyond Scale: The Diversity Coefficient as a Data Quality Metric for Variability in Natural Language Data
by: Miranda, Brando, et al.
Published: (2023)

Correlating and Predicting Human Evaluations of Language Models from Natural Language Processing Benchmarks
by: Schaeffer, Rylan, et al.
Published: (2025)

Diffusion Stochastic Optimization for Min-Max Problems
by: Cai, Haoyuan, et al.
Published: (2024)

StyDeSty: Min-Max Stylization and Destylization for Single Domain Generalization
by: Liu, Songhua, et al.
Published: (2024)

A Novel Gaussian Min-Max Theorem and its Applications
by: Akhtiamov, Danil, et al.
Published: (2024)

Statistical Mechanics of Min-Max Problems
by: Ichikawa, Yuma, et al.
Published: (2024)

Robust Variational Bayes by Min-Max Median Aggregation
by: Yan, Jiawei, et al.
Published: (2025)

Enhancing Privacy in Decentralized Min-Max Optimization: A Differentially Private Approach
by: Quan, Yueyang, et al.
Published: (2025)

ZIP-FIT: Embedding-Free Data Selection via Compression-Based Alignment
by: Obbad, Elyas, et al.
Published: (2024)

Near-Optimal Privacy-Preserving Learning for Max-Min Fair Multi-Agent Bandits
by: Leshem, Amir
Published: (2023)

Collaborative Min-Max Regret in Grouped Multi-Armed Bandits
by: Blanchard, Moïse, et al.
Published: (2025)

Constrained Multi-Objective Reinforcement Learning with Max-Min Criterion
by: Park, Giseung, et al.
Published: (2026)

Implicit Riemannian Optimism with Applications to Min-Max Problems
by: Roux, Christophe, et al.
Published: (2025)

Why Has Predicting Downstream Capabilities of Frontier AI Models with Scale Remained Elusive?
by: Schaeffer, Rylan, et al.
Published: (2024)