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Recipes for Steering and Scaling LLMs via Sampling
Author1, Author2, Author3, Author4, Author5
samplingautoregressive modelsprobabilistic inference
2608.26120
Builder Relevance
1h ago80%
Abstract
This paper presents a framework for improving sampling efficiency in Large Language Models (LLMs) through two novel algorithms.
Reality Card
Core Claim
The proposed algorithms based on Sequential Monte Carlo and Replica Exchange significantly improve the generation quality of LLMs without requiring external supervision.
Method / Result
The new methods outperform Best-of-N and standard MCMC baselines in scaling generation quality.
Limitations
The paper does not provide extensive details on the implementation of the algorithms, which may hinder reproducibility.
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