Effective and Efficient Reasoning in Large Language Models: A Survey of Test-Time Reasoning Strategies and Optimization

PhD Qualifying Examination


Title: "Effective and Efficient Reasoning in Large Language Models: A
Survey of Test-Time Reasoning Strategies and Optimization"

by

Miss Eunseo JUNG


Abstract:

The reasoning capabilities of large language models (LLMs) have improved 
substantially through increases in model scale, training data, and 
post-training optimization. However, further gains through training-time 
scaling require substantial computational, financial, and engineering 
resources, limiting the practical viability of this approach. This constraint 
has shifted attention toward test-time reasoning, where additional 
inference-time computation is used to construct intermediate reasoning steps, 
explore alternative solutions, or interact with external tools.

However, the growing reliance on inference-time computation introduces a 
second scaling problem where additional reasoning computation is not 
necessarily used effectively. Models may over-reason on simple problems or 
invoke tools and verifiers whose overhead yields little improvement. 
Conversely, difficult problems may receive insufficient computation under 
fixed inference budgets. Reasoning effectiveness and inference efficiency are 
therefore tightly coupled. Yet their methods are often studied separately, 
which makes it difficult to obtain a coherent view of the trade-off between 
reasoning performance and the computational resources required to achieve it. 
A unified perspective is needed to connect these complementary research 
directions.

This survey addresses this need by examining test-time reasoning through 
complementary perspectives on effectiveness and efficiency. We develop a 
unified taxonomy that distinguishes methods for improving reasoning quality 
from those for optimizing the use of inference-time computation, while 
recognizing that these roles can overlap. Using this framework, we compare 
representative approaches in terms of their mechanisms, empirical benefits, 
resource requirements, assumptions, and limitations, and identify open 
challenges in adaptive computation, reasoning reliability, and cost-aware 
evaluation.


Date:                   Friday, 21 August 2026

Time:                   2:00pm - 4:00pm

Venue:                  Room 3494
                        Lift 25/26

Committee Members:      Prof. Lei Chen (Supervisor)
                        Prof. Xiaofang Zhou (Chairperson)
                        Prof. Ke Yi