Image de couverture de livre

Optimal Stopping vs Best-of-$N$ for Inference Time Optimization

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Kalayci, Yusuf, Raman, Vinod, Dughmi, Shaddin
Format:	Preprint
Publié:	2025
Sujets:	Machine Learning Computation and Language
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Adaptive Generate-Rank-Verify: Inference-Time Search with Costly Verification
par: Dughmi, Shaddin, et autres
Publié: (2026)

Is Transductive Learning Equivalent to PAC Learning?
par: Dughmi, Shaddin, et autres
Publié: (2024)

A Theory of Time-Sensitive Language Generation: Sparse Hallucination Beats Mode Collapse
par: Ganju, Atul, et autres
Publié: (2026)

AdaBoN: Adaptive Best-of-N Alignment
par: Raman, Vinod, et autres
Publié: (2025)

PAC Learning is just Bipartite Matching (Sort of)
par: Dughmi, Shaddin
Publié: (2025)

Near-Optimal Sparsifiers for Stochastic Knapsack and Assignment Problems
par: Dughmi, Shaddin, et autres
Publié: (2025)

Relatively Smart: A New Approach for Instance-Optimal Learning
par: Dughmi, Shaddin, et autres
Publié: (2026)

Limitations of Stochastic Selection with Pairwise Independent Priors
par: Dughmi, Shaddin, et autres
Publié: (2023)

Local Regularizers Are Not Transductive Learners
par: Jafar, Sky, et autres
Publié: (2025)

Regularization and Optimal Multiclass Learning
par: Asilis, Julian, et autres
Publié: (2023)

Representative Language Generation
par: Peale, Charlotte, et autres
Publié: (2025)

Structured Pruning for Diverse Best-of-N Reasoning Optimization
par: Nguyen, Hieu Trung, et autres
Publié: (2025)

TreeBoN: Enhancing Inference-Time Alignment with Speculative Tree-Search and Best-of-N Sampling
par: Qiu, Jiahao, et autres
Publié: (2024)

LYNX: Learning Dynamic Exits for Confidence-Controlled Reasoning
par: Akgül, Ömer Faruk, et autres
Publié: (2025)

Learning to Choose or Choosing to Learn: Best-of-N vs. Supervised Fine-Tuning for Bit String Generation
par: Somerstep, Seamus, et autres
Publié: (2025)

Transductive Learning Is Compact
par: Asilis, Julian, et autres
Publié: (2024)

Efficient Multi-Agent Delegated Search
par: Bechtel, Curtis, et autres
Publié: (2024)

Inference-Aware Fine-Tuning for Best-of-N Sampling in Large Language Models
par: Chow, Yinlam, et autres
Publié: (2024)

Inference Scaling vs Reasoning: An Empirical Analysis of Compute-Optimal LLM Problem-Solving
par: AbdElhameed, Marwan, et autres
Publié: (2024)

Best-of-N Jailbreaking
par: Hughes, John, et autres
Publié: (2024)

Just on Time: Token-Level Early Stopping for Diffusion Language Models
par: Kohut, Zahar, et autres
Publié: (2026)

GenSelect: A Generative Approach to Best-of-N
par: Toshniwal, Shubham, et autres
Publié: (2025)

FLOP-Efficient Training: Early Stopping Based on Test-Time Compute Awareness
par: Amer, Hossam, et autres
Publié: (2026)

Proper Learnability and the Role of Unlabeled Data
par: Asilis, Julian, et autres
Publié: (2025)

Variational Best-of-N Alignment
par: Amini, Afra, et autres
Publié: (2024)

Majority of the Bests: Improving Best-of-N via Bootstrapping
par: Rakhsha, Amin, et autres
Publié: (2025)

Tracking the Best Expert Privately
par: Saha, Aadirupa, et autres
Publié: (2025)

Learning Generative Selection for Best-of-N
par: Toshniwal, Shubham, et autres
Publié: (2026)

MarkovScale: Towards Optimal Sequential Scaling at Inference Time
par: Wang, Youkang, et autres
Publié: (2026)

TERMINATOR: Learning Optimal Exit Points for Early Stopping in Chain-of-Thought Reasoning
par: Nagle, Alliot, et autres
Publié: (2026)

AI-rithmetic
par: Bie, Alex, et autres
Publié: (2026)

BOND: Aligning LLMs with Best-of-N Distillation
par: Sessa, Pier Giuseppe, et autres
Publié: (2024)

BoNBoN Alignment for Large Language Models and the Sweetness of Best-of-n Sampling
par: Gui, Lin, et autres
Publié: (2024)

Stopping Computation for Converged Tokens in Masked Diffusion-LM Decoding
par: Oba, Daisuke, et autres
Publié: (2026)

Beyond Chinchilla-Optimal: Accounting for Inference in Language Model Scaling Laws
par: Sardana, Nikhil, et autres
Publié: (2023)

The Solution for the AIGC Inference Performance Optimization Competition
par: Pan, Sishun, et autres
Publié: (2024)

Stop-Think-AutoRegress: Language Modeling with Latent Diffusion Planning
par: Lovelace, Justin, et autres
Publié: (2026)

Efficient Prompt Optimization Through the Lens of Best Arm Identification
par: Shi, Chengshuai, et autres
Publié: (2024)

Correct and Optimal: the Regular Expression Inference Challenge
par: Valizadeh, Mojtaba, et autres
Publié: (2023)

Compute Optimal Scaling of Skills: Knowledge vs Reasoning
par: Roberts, Nicholas, et autres
Publié: (2025)