Hugging Face Trending Papers
Jul 20

Token-Level Off-Policy Learning for Faithful Generation Under Distribution Shift

We propose Token-Level Off-Policy Labeling (TOPL), an off-policy training paradigm that reframes post-training as a token-level correctness prediction task. Our key intuition is that by training the model to distinguish good and bad tokens in a response, we naturally guide the model towards generating good tokens, while avoiding the pitfalls that come with directly training the model to generate off-policy tokens.

arXiv Machine Learning
Aug 13

Weightless Fine-Tuning: Personalizing LLMs via Logit-Space Transport

arXiv:2608. 11342v1 Announce Type: new Abstract: Supervised fine-tuning (SFT) is a standard approach for adapting LLMs to a target distribution, but in settings such as personalization, where each author requires separate weight access, optimization, storage, and retraining, its costs become prohibitive.

By Bohan Zhang, Anqi Ni, Yixin Wang, Paramveer S. Dhillon
arXiv Computation and Language
Oct 16

No Single Tokenizer Feature Reliably Predicts Downstream Language Model Performance

The paper investigates whether intrinsic tokenizer features can predict the performance of downstream language models. By training about 200 models across nine languages and evaluating numerous tokenizer metrics, the authors find that none of the existing metrics consistently forecasts model performance across different families, tasks, and languages. They propose a new linguistically grounded metric that captures aspects of tokenizer quality missed by prior measures, though its predictive power may still vary.

By Antonio Lopardo, Luis Frentzen Salim, Shikhar Srivastava, Kevin Jiang, Mridul Sharma, Catherine Arnett