Efficient Reasoning for Large Reasoning Model

PhD Qualifying Examination


Title: "Efficient Reasoning for Large Reasoning Model"

by

Mr. Haoyue ZHANG


Abstract:

The rapid advancement of Large Language Models (LLMs) has significantly 
enhanced their capabilities in natural language understanding and complex 
reasoning tasks. However, the extensive use of Chain-of-Thought (CoT) 
prompting often leads to overly verbose reasoning processes, resulting in 
high computational costs and latency that hinder deployment in real-time, 
computation-sensitive applications. This paper provides a comprehensive 
survey on efficient reasoning methodologies for Large Reasoning Models 
(LRMs). In this paper, we introduce training-based approaches, including 
Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL), as well as 
innovative reasoning paradigms such as Prompt-guided efficient reasoning, 
switching of thinking, collaborative reasoning, and latent reasoning. 
Additionally, evaluation metrics and benchmarks tailored to assess reasoning 
efficiency are also examined. Finally, future research directions aimed at 
further optimizing reasoning processes in LRMs are discussed, including 
ensuring multi-modal application, scalability, reducing resource 
consumption, and addressing associated safety concerns.


Date:                   Thursday, 14 August 2025

Time:                   9:00am - 11:00am

Venue:                  Room 3494
                        Lifts 25/26

Committee Members:      Prof. Song Guo (Supervisor)
                        Prof. Raymond Wong (Chairperson)
                        Prof. Ke Yi