Q*: A Versatile Artificial Intelligence AI Approach to Improve LLM Performance in Reasoning Tasks
Large Language Models (LLMs) face challenges in complex reasoning tasks due to errors, hallucinations, and inconsistencies. Q* is a robust framework designed to enhance the multi-step reasoning capabilities of LLMs through deliberative planning. It introduces general methods for estimating optimal Q-values of state-action pairs and employs plug-and-play Q-value models as heuristic functions within an A* search framework to guide LLMs efficiently.
Practical Solutions and Value
Q* demonstrated significant performance improvements across various reasoning tasks, surpassing traditional methods and some closed-source models. It enhances LLMs’ ability to solve complex problems that require in-depth, logical thinking beyond simple auto-regressive token generation. Q* uses a versatile Q-value model trained solely on ground-truth data, making it easily adaptable to various reasoning tasks without modifications. The framework’s agility and superior performance highlight its potential to improve LLMs’ complex problem-solving capabilities significantly.
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