System-1.5 Reasoning: Traversal in Language and Latent Spaces with Dynamic Shortcuts
Xiaoqiang Wang, Suyuchen Wang, Yun Zhu, Bang Liu
2025-05-30
Summary
This paper talks about System-1.5 Reasoning, a new technique that helps big language models think and answer questions faster by taking smart shortcuts during their calculations.
What's the problem?
The problem is that large language models often take a lot of time and computer power to come up with answers because they process every step in detail, even when it's not always necessary. This slows things down and makes it harder to use these models efficiently.
What's the solution?
The researchers developed a way for the model to decide, while it's working, which parts of the problem need more attention and which can be handled with quick shortcuts. By doing this in the model's hidden, or 'latent,' space, they make the whole process faster and require fewer steps to generate good answers.
Why it matters?
This is important because it means AI can deliver results much more quickly and use less energy, making it more practical for real-world applications like chatbots, virtual assistants, and other tools that need to respond in real time.
Abstract
System-1.5 Reasoning improves the efficiency and performance of large language models by dynamically allocating computation through adaptive shortcuts in latent space, leading to faster inference and reduced token generation.