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A new framework from researchers at the University of Illinois, Urbana-Champaign, and the University of California, Berkeley gives developers more control over how large language models (LLMs) “think,” improving their reasoning capabilities while making more efficient use of their inference budget.

The framework, called AlphaOne (α1), is a test-time scaling technique, tweaking a model’s behavior during inference without needing costly retraining. It provides a universal method for modulating the reasoning process of advanced LLMs, offering developers the flexibility to improve performance on complex tasks in a more controlled and cost-effective manner than existing approaches.

The challenge of slow thinking

In recent years, developers of large reasoning models (LRMs), such as OpenAI o3 and DeepSeek-R1, have incorporated mechanisms inspired by “System 2” thinking—the slow, deliberate, and logical mode of human cognition. This is distinct from “System 1” thinking, which is fa...