Hugging Face Trending Papers July 20, 2026

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

Read the original on Hugging Face Trending Papers →

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.

Summary generated by The Flow from the publisher's feed. The full article lives at Hugging Face Trending Papers.

arXiv Machine Learning

Jul 21

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

arXiv:2607. 17524v1 Announce Type: cross Abstract: We propose Token-Level Off-Policy Labeling (TOPL), an off-policy training paradigm that reframes post-training as a token-level correctness prediction task.

By Zitong Huang, Gustavo Lucas Carvalho, Deqing Fu, Robin Jia

nlp fine-tuning benchmarks

arXiv AI

Aug 6

Calibrating Transformer Attention via Task-Space Sensitivity Feedback

arXiv:2512. 20661v2 Announce Type: replace Abstract: Transformer-based pre-trained language models (PLMs) excel in text classification but suffer from attention dilution and attention sink effects, forcing models to over-focus on task-irrelevant tokens.

By Yawei Liu

llms nlp reinforcement-learning benchmarks safety

arXiv Machine Learning

Jun 29

EntMTP: Accelerating LLM Inference with Entropy Guided Multi Token Prediction

arXiv:2606. 27550v1 Announce Type: cross Abstract: Multi-token prediction has been shown to increase data density during training, improve downstream text-generation quality, and serves as the defacto approach for self-speculative decoding.

By Carrie Chen

llms efficiency benchmarks

arXiv Machine Learning

Jun 4

Data Attribution in Large Language Models via Bidirectional Gradient Optimization

arXiv:2606. 04928v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly deployed across diverse applications, raising critical questions for governance, accountability, and data provenance.

By Fr\'ed\'eric Berdoz, Luca A. Lanzend\"orfer, Kaan Bayraktar, Roger Wattenhofer

llms safety

arXiv AI

Jul 7

Evolutionary Guided Decoding: Iterative Value Refinement for LLMs

arXiv:2503. 02368v4 Announce Type: replace-cross Abstract: While guided decoding, especially value-guided methods, has emerged as a cost-effective alternative for controlling language model outputs without re-training models, its effectiveness is limited by the accuracy of the value function.

By Zhenhua Liu, Lijun Li, Ruizhe Chen, Yuxian Jiang, Tong Zhu, Zhaochen Su, Wenliang Chen, Jing Shao

llms nlp safety

arXiv Machine Learning

Aug 3

PTP: Previous-Token Prediction based LLM Inversion for Near-Exact Prompt Reconstruction

arXiv:2607. 29378v1 Announce Type: cross Abstract: Large language models (LLMs) generate text by auto-regressively sampling the next token.

By Pirzada Suhail, Nagasai Saketh Naidu, Atanu R Sinha, Amit Sethi

llms fine-tuning

Related stories

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

Calibrating Transformer Attention via Task-Space Sensitivity Feedback

EntMTP: Accelerating LLM Inference with Entropy Guided Multi Token Prediction

Data Attribution in Large Language Models via Bidirectional Gradient Optimization

Evolutionary Guided Decoding: Iterative Value Refinement for LLMs

PTP: Previous-Token Prediction based LLM Inversion for Near-Exact Prompt Reconstruction