From Efficiency to Adaptivity: A Deeper Look at Adaptive Reasoning in Large Language Models

Researchers present a new framework for adaptive reasoning in large language models, addressing the problem that current LLMs use uniform reasoning strategies regardless of task complexity. The survey formalizes adaptive reasoning as a control-augmented policy optimization problem and proposes a taxonomy of training-based and training-free approaches to achieve more efficient reasoning allocation.

• →Current LLMs inefficiently apply uniform reasoning strategies to all problems regardless of difficulty level.
• →Adaptive reasoning is formalized as balancing task performance with computational cost based on input characteristics.
• →The framework distinguishes between training-based approaches using reinforcement learning and training-free methods using prompt conditioning.
• →Researchers connect classical cognitive reasoning paradigms with their algorithmic implementations in LLMs.
• →Key challenges remain in self-evaluation, meta-reasoning, and human-aligned reasoning control.