# @awomanindatascience on Instagram

- **Type:** Video
- **Original URL:** https://www.instagram.com/p/DeKJY0gtaJm
- **Gondola URL:** https://gondola.cc/posts/71535733-awomanindatascience-instagram
- **Thumbnail:** https://img.gondola.cc/tr:w-,h-,fo-auto/postThumbnails/9def773834.jpg
- **Posted:** 2026-10-06T15:50:05.000+00:00
- **Account Owner:** Chinar Arora (@awomanindatascience) — https://gondola.cc/awomanindatascience

## Caption

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

## Stats

- **Views:** 1,113
- **Likes:** 3
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- **Comments:** 1

## Tags

llm, chainofthought, reasoningmodels, machinelearning, aiengineer

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