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cs.CL2026
When to Think Deeply: Inhibitory Deliberation for LLM Reasoning
Zhixuan He, Yue Feng
Reasoning Large Language Models can improve problem-solving performance through deliberative inference, but invoking slow reasoning for every input is computationally expensive and…
cs.CL2025
Unleashing Diverse Thinking Modes in LLMs through Multi-Agent Collaboration
Zhixuan He, Yue Feng
Large Language Models (LLMs) demonstrate strong performance but often lack interpretable reasoning. This paper introduces the Multi-Agent Collaboration Framework for Diverse Thinki…
cs.CL2024★ 4 cited
A Survey on Human Preference Learning for Large Language Models
Ruili Jiang, Kehai Chen, Xuefeng Bai +6
The recent surge of versatile large language models (LLMs) largely depends on aligning increasingly capable foundation models with human intentions by preference learning, enhancin…