19 citations · 66 across the 11 of their papers we have counts for
5 papers · 1 filter
Emergence of Superposition: Unveiling the Training Dynamics of Chain of Continuous Thought
Hanlin Zhu, Shibo Hao, Zhiting Hu +3
Previous work shows that the chain of continuous thought (continuous CoT) improves the reasoning capability of large language models (LLMs) by enabling implicit parallel thinking,…
Vision-G1: Towards General Vision Language Reasoning with Multi-Domain Data Curation
Yuheng Zha, Kun Zhou, Yujia Wu +7
Despite their success, current training pipelines for reasoning VLMs focus on a limited range of tasks, such as mathematical and logical reasoning. As a result, these models face d…
Revisiting Reinforcement Learning for LLM Reasoning from A Cross-Domain Perspective
Zhoujun Cheng, Shibo Hao, Tianyang Liu +21
Reinforcement learning (RL) has emerged as a promising approach to improve large language model (LLM) reasoning, yet most open efforts focus narrowly on math and code, limiting our…
Reasoning by Superposition: A Theoretical Perspective on Chain of Continuous Thought
Hanlin Zhu, Shibo Hao, Zhiting Hu +3
Large Language Models (LLMs) have demonstrated remarkable performance in many applications, including challenging reasoning problems via chain-of-thoughts (CoTs) techniques that ge…
LLM Pretraining with Continuous Concepts
Jihoon Tack, Jack Lanchantin, Jane Yu +7
Next token prediction has been the standard training objective used in large language model pretraining. Representations are learned as a result of optimizing for token-level perpl…