Author(s): Tarun Singh

Originally published on Towards AI.

Why the future of AI isn’t bigger models — it’s smarter reasoning.

TL;DR: Reasoning models are the next jump after “just bigger LLMs.” Instead of prompt tricks, they win with reinforcement learning on verifiable tasks, test-time compute, and distillation. In this article you’ll (1) understand the shift, (2) see exactly when reasoning models beat classic LLMs, and (3) get a copy-paste Python pipeline: generate verifiable math/coding tasks, train a tiny RL loop with programmatic rewards, evaluate with pass@k, and serve locally with vLLM + quantization. If you want fast, cheap, and practical reasoning — read on.

This article discusses the transition from large language models (LLMs) to reasoning models, emphasizing the importance of reinforcement learning, verifiable tasks, and efficient training techniques. It explores different aspects of reasoning models, including their advantages over traditional models, the use of structured compute, and the implications for AI deployment. Readers are introduced to practical applications such as creating and training these models, with a focus on performance metrics and cost-efficient methodologies for real-world applications.

Read the full blog for free on Medium.

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Published via Towards AI