3 papers
cs.LG2026
How Language Models Differ in Redistributing Attention-Head Activity Under Serial Demand
Johnny Jingze Li, Abdulla Kuleib, Kalyan Basu +1
The way a model distributes activity over each layer's attention heads offers a coarse view of how it routes information through depth; how this changes with the task is part of wh…
cs.MA2026
Agents of Diffusion: Enhancing Diffusion Language Models with Multi-Agent Reinforcement Learning for Structured Data Generation (Extended Version)
Aja Khanal, Kaushik T. Ranade, Rishabh Agrawal +2
Generating high-quality structured data such as JSON records, remains a fundamental challenge for large language models (LLMs), particularly when semantic richness must coexist wit…
cs.LG2024
Premier-TACO is a Few-Shot Policy Learner: Pretraining Multitask Representation via Temporal Action-Driven Contrastive Loss
Ruijie Zheng, Yongyuan Liang, Xiyao Wang +7
We present Premier-TACO, a multitask feature representation learning approach designed to improve few-shot policy learning efficiency in sequential decision-making tasks. Premier-T…