Stop Overthinking: A Survey on Efficient Reasoning for Large Language Models (Paper Review)
Last Updated on October 28, 2025 by Editorial Team
Author(s): Hira Ahmad
Originally published on Towards AI.
Stop Overthinking: A Survey on Efficient Reasoning for Large Language Models (Paper Review)
Intelligence is not in thinking long, it’s in thinking right.
In the race to make machines reason like humans, we’ve trained models to think deeply but often, they end up thinking too much.
This article explores the dilemma of overthinking in Large Language Models (LLMs) and emphasizes the importance of efficient reasoning. It discusses how modern reasoning models often misinterpret depth as a measure of quality, leading to increased costs and decreased clarity. Various efficiency-focused approaches are proposed, emphasizing the necessity of training models to know when to stop reasoning. Future advancements in AI are positioned to redefine intelligence by focusing on smarter, not just deeper, reasoning.
Read the full blog for free on Medium.
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Note: Article content contains the views of the contributing authors and not Towards AI.
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