Harnessing the Reasoning Economy: A Survey of Efficient Reasoning for Large Language Models

Key points

Current LLMs show inefficient reasoning behaviors such as excessive computation and unnecessary deliberation.
There is a lack of systematic understanding of how to optimize the tradeoff between reasoning ability and compute cost.
The survey develops a structured framework that classifies reasoning inefficiency and potential solutions.
It classifies optimization approaches into behavior regulation after training and usage improvement at test time.
Reasoning inefficiency arises from both model behavior, such as length bias and deceptive thinking, and model usage, such as poor algorithm choice and compute allocation.
The proposed solutions should address both post-training factors, such as data quality and structure, and test-time strategies such as budget allocation and adaptive decoding.

External research summaries. These are not HDATF publications or measured product results.