---
title: "Why We Think"
url: https://daily.dev/posts/why-we-think-tojbimbhw
source_url: https://lilianweng.github.io/posts/2025-05-01-thinking/
type: article
source: "Lil’Log"
published: 2025-05-17T09:06:15.223Z
updated: 2025-05-17T09:06:29.187Z
tags: ["machine-learning", "deep-learning", "nlp", "reinforcement-learning"]
reading_time: 18
upvotes: 5
comments: 0
language: en
---

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# Why We Think

**[Lil’Log](https://daily.dev/sources/lilianweng)** · 18 min read · 5 upvotes · 0 comments

## Summary

Recent developments in AI have focused on the use of test-time compute and chain-of-thought (CoT) to improve model performance by emulating human-like thinking processes, which involve both fast and slow thought modes. Techniques like parallel sampling and sequential revision are being explored for enhancing the decoding process, with reinforcement learning showing promise in training models capable of advanced reasoning. The integration of external tools such as code interpreters further boosts problem-solving capabilities. Interpretability and the accuracy of these methods are crucial, as biases can arise if models do not faithfully represent their reasoning processes.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://lilianweng.github.io/posts/2025-05-01-thinking/>

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---

Tags: [#machine-learning](https://daily.dev/tags/machine-learning), [#deep-learning](https://daily.dev/tags/deep-learning), [#nlp](https://daily.dev/tags/nlp), [#reinforcement-learning](https://daily.dev/tags/reinforcement-learning)

[View this post on daily.dev](https://daily.dev/posts/why-we-think-tojbimbhw)
