In this video, Jaydeep dives deep into Large Reasoning Models (LRMs) and explains how they differ from traditional Large Language Models (LLMs). Learn how modern AI systems “think before they answer,” using internal planning, step-by-step evaluation, reinforcement learning, and test-time compute to improve accuracy and logical coherence.
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The video breaks down key concepts like chain-of-thought reasoning, process reward models, model distillation, thinking budgets, and real-world trade-offs between speed and accuracy.
It also helps you understand when to use LRMs vs LLMs in practical AI use cases, especially for complex analysis, automation, and decision-making scenarios.
Video Chapters
00:00 Introduction
01:42 What are Large Reasoning Models
03:36 LLMs vs LRMs
05:00 How Large Reasoning Models Are Built
09:14 Thinking Budget, Cost & Real-World Trade-offs
13:11 When to Use LRMs + Final Insights
