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cs.LG2026
Shared Weights, Selected Computations: How Looped Transformers Route What Each Loop Does
Jiaju Wu, Yi Hu, Muhan Zhang
Looped Transformers repeatedly apply the same set of Transformer layers, giving them a recurrent architecture for latent computation. Their strong performance on iterative reasonin…
cs.LG2026
The Expressivity Boundary of Probabilistic Circuits: A Comparison with Large Language Models
Zhiyu Zhao, Xuejie Liu, Muhan Zhang +1
Probabilistic Circuits (PCs) are deep generative models that support exact and efficient probabilistic inference. Yet in autoregressive language modeling, PCs still lag behind Tran…
cs.LG2025
The Road Less Traveled: Enhancing Exploration in LLMs via Sequential Sampling
Shijia Kang, Muhan Zhang
Reinforcement learning (RL) has been pivotal in enhancing the reasoning capabilities of large language models (LLMs), but it often suffers from limited exploration and entropy coll…