Day 5 of Building a Reasoning Model from Scratch What if you could make an LLM smarter without changing a single weight? That’s inference-time scaling: letting a trained model use more compute when it answers. Today I tried the simplest version, chain-of-thought prompting, on a Qwen 0.6B parameter model. Without it, the model got a simple MATH-500 algebra problem wrong. Adding “Explain step by step” got it right. Why it works: - Writing out the steps gives the model more chances to catch its own mistakes - It matches the step-by-step solutions the model saw in training The tradeoff: - More tokens means more latency and cost - It isn’t always right, and it can overthink easy problems Next up: temperature scaling and top-p filtering. Following Sebastian Raschka’s Build a Reasoning Model (From Scratch). Paper: Kojima et al., 2022, “Large Language Models are Zero-Shot Reasoners.” #LLM #ReasoningModels #ChainOfThought #MachineLearning #AIEngineer GenAI