Don't Overthink It: A Survey of Efficient R1-style Large Reasoning Models
Linan Yue, Yichao Du, Yizhi Wang, Weibo Gao, Fangzhou Yao, Li Wang, Ye Liu, Ziyu Xu, Qi Liu, Shimin Di, Min-Ling Zhang
2025-08-08
Summary
This paper talks about efficient reasoning methods for Large Reasoning Models (LRMs), focusing on R1-style models that learn to think step-by-step and improve their answers by trying different reasoning paths.
What's the problem?
The problem is that some LRMs generate very long and complicated reasoning steps that are often repetitive or unnecessary, which makes them slow and waste resources while trying to solve problems.
What's the solution?
The solution was to study how R1-style models use reinforcement learning with rule-based rewards to teach themselves shorter, clearer, and more effective reasoning processes. These models can reflect, self-check, and improve their reasoning without needing examples to learn from at the start.
Why it matters?
This matters because efficient reasoning models can provide correct answers faster and with less computing power, making AI more practical and accessible for solving complex real-world problems.
Abstract
Research on efficient reasoning methods for Large Reasoning Models (LRMs) aims to reduce reasoning path length without sacrificing performance, through single-model optimization and model collaboration.