From LLMs to Reasoning Models: Why models that “think” matter

Introduction

The fundamental distinction between LLMs and Reasoning Models lies in their optimization goal. An LLM seeks to model the statistical distribution of natural language; a reasoning model seeks to solve problems through structured inference.

This shift is comparable to the move from heuristic models to algorithms with formal guarantees. In a business context, it implies moving from conversational assistants to systems capable of analyzing scenarios, justifying decisions, and verifying results.

The limitations of LLMs in complex reasoning are observed in tasks involving mathematics, deep debugging, logistics planning, or causal analysis. Although they can generate plausible answers, they lack internal mechanisms to verify logical consistency or explore alternatives.

The objective of this document is to offer an in-depth technical analysis of architectures, reasoning mechanisms, evaluation metrics, and implementation considerations relevant to research and engineering teams.

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