6 papers
Transformers Provably Learn to Internalize Chain-of-Thought
Yixiao Huang, Hanlin Zhu, Zixuan Wang +4
Chain-of-Thought (CoT) prompting substantially improves the sample efficiency of transformers, reducing the complexity of tasks like parity learning from exponential to polynomial…
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,…
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…
GSM-Agent: Understanding Agentic Reasoning Using Controllable Environments
Hanlin Zhu, Tianyu Guo, Song Mei +4
As LLMs are increasingly deployed as agents, agentic reasoning - the ability to combine tool use, especially search, and reasoning - becomes a critical skill. However, it is hard t…
Safe Learning Under Irreversible Dynamics via Asking for Help
Benjamin Plaut, Juan Liévano-Karim, Hanlin Zhu +1
Most learning algorithms with formal regret guarantees essentially rely on trying all possible behaviors, which is problematic when some errors cannot be recovered from. Instead, w…
Avoiding Catastrophe in Online Learning by Asking for Help
Benjamin Plaut, Hanlin Zhu, Stuart Russell
Most learning algorithms with formal regret guarantees assume that all mistakes are recoverable and essentially rely on trying all possible behaviors. This approach is problematic…