Meta-Reasoner: Dynamic Guidance for Optimized Inference-time Reasoning in Large Language Models

Understand

Large Language Models (LLMs) often struggle with computational efficiency and error propagation in multi-step reasoning tasks.

• While recent advancements on prompting and post-training have enabled LLMs to perform step-wise reasoning, they still tend to explore unproductive solution paths without effective backtracking or strategy adjustment.
• In this paper, we propose Meta-Reasoner, a new framework that empowers LLMs to "think about how to think".
• It optimizes the inference process by dynamically adapting reasoning strategies in real-time.