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[2505.22650] On Learning Verifiers and Implications to Chain-of-Thought Reasoning
Summary
This paper explores learning verifiers for Chain-of-Thought reasoning in natural language, addressing the challenges of incorrect inferences in complex problem-solving.
Why It Matters
As Chain-of-Thought reasoning becomes essential in AI, ensuring the reliability of these processes is crucial. This research provides a formal framework for developing verifiers that can validate reasoning steps, enhancing the robustness of AI systems in solving complex tasks.
Key Takeaways
- Chain-of-Thought reasoning can lead to incorrect inferences in AI.
- The paper proposes a PAC-learning framework for developing reliable verifiers.
- Sample complexity bounds are provided for learning effective verifiers.
- Different verification goals are analyzed to enhance reasoning reliability.
- The study highlights limitations in learning certain verification objectives.
Computer Science > Machine Learning arXiv:2505.22650 (cs) [Submitted on 28 May 2025 (v1), last revised 13 Feb 2026 (this version, v2)] Title:On Learning Verifiers and Implications to Chain-of-Thought Reasoning Authors:Maria-Florina Balcan, Avrim Blum, Zhiyuan Li, Dravyansh Sharma View a PDF of the paper titled On Learning Verifiers and Implications to Chain-of-Thought Reasoning, by Maria-Florina Balcan and 3 other authors View PDF HTML (experimental) Abstract:Chain-of-Thought reasoning has emerged as a powerful approach for solving complex mathematical and logical problems. However, it can often veer off track through incorrect or unsubstantiated inferences. Formal mathematical reasoning, which can be checked with a formal verifier, is one approach to addressing this issue. However, currently LLMs are simply not good enough to solve complex problems in a formal way, and even just formalizing an informal problem statement can be challenging. Motivated by this fact, in this work we consider the problem of learning reliable verifiers for natural language Chain-of-Thought reasoning. That is, given a problem statement and step-by-step solution in natural language, the aim of the verifier is to output [Yes] if the reasoning steps in the solution are all valid, and [No] otherwise. In this work we give a formal PAC-learning framework for studying this problem. We propose and analyze several natural verification goals, at different levels of strength, in this framework. We provide ...
