Dyve: Thinking Fast and Slow for Dynamic Process Verification
Jianyuan Zhong, Zeju Li, Zhijian Xu, Xiangyu Wen, Qiang Xu
2025-02-18
Summary
This paper talks about Dyve, a new AI system that improves how large language models (LLMs) detect errors in reasoning by combining two types of thinking: fast, instinctive decisions and slow, careful analysis. It is inspired by how humans use both quick judgments and detailed reasoning to solve problems.
What's the problem?
Current AI systems often struggle with detecting errors in complex reasoning tasks because they rely on a single approach to thinking. This makes them less effective at handling both simple and complicated steps in processes, leading to mistakes in areas like verifying workflows or solving advanced math problems.
What's the solution?
The researchers created Dyve, which uses a dual-process system inspired by Daniel Kahneman's theory of fast and slow thinking. For easy tasks, Dyve uses a quick, instinctive method (System 1), while for harder tasks, it switches to a more detailed and deliberate approach (System 2). They also introduced a new training method that filters out noisy data and uses techniques like Monte Carlo estimation to improve accuracy. Tests on benchmarks like ProcessBench and the MATH dataset showed that Dyve performs significantly better than existing systems at detecting reasoning errors.
Why it matters?
This matters because Dyve could make AI systems much more reliable for tasks that involve complex reasoning, such as verifying business processes or solving mathematical problems. By combining fast and slow thinking, it mimics human problem-solving more closely, paving the way for smarter and more adaptable AI systems.
Abstract
We present Dyve, a dynamic process verifier that enhances reasoning error detection in large language models by integrating fast and slow thinking, inspired by Kahneman's Systems Theory. Dyve adaptively applies immediate token-level confirmation System 1 for straightforward steps and comprehensive analysis System 2 for complex ones. Leveraging a novel step-wise consensus-filtered process supervision technique, combining Monte Carlo estimation with LLM based evaluation, Dyve curates high-quality supervision signals from noisy data. Experimental results on ProcessBench and the MATH dataset confirm that Dyve significantly outperforms existing process-based verifiers and boosts performance in Best-of-N settings.