This week, I was curious about the OpenAI o1 model and wanted to know more. In this blog post, I am going to share my thoughts on the o1 model, the different terminologies I learned, and why it is referred to as “thinking AI.” I hope you enjoy!

Alot of things to discuss from that released, but today I am going to focus on chain of thought reasoning and how it makes o1 even better

The OpenAI o1 model, introduced in September 2024, represents a significant advancement in artificial intelligence. Unlike previous models, o1 is designed to spend more time “thinking” before it responds, making it exceptionally strong in complex reasoning tasks, science, coding, and math,

Well, as researched and studied about the o1 model , I came accross some terminologies that stack together to explain why we called this a "a thinking model". Some of which include - chain of thought, Test Time Compute, ReInforcement Learning,

1. What is Chain of Thought?

Chain of thought refers to a reasoning process where the AI model breaks down complex problems into simpler, intermediate steps before arriving at a final answer. This approach mimics human problem-solving, where thinking out loud or writing down steps can lead to more accurate solutions.

Chain Of Thought before o1

Before the development of the o1 model, chain of thought in AI models like GPT-3 and GPT-4 was primarily achieved through prompting techniques, where the model was guided to break down problems into smaller steps. This method helped improve reasoning but was not inherently built into the model’s core functionality. For example, in real life, solving a complex math problem often involves writing down intermediate steps to reach the final solution. Similarly, earlier AI models could be prompted to follow a step-by-step approach, but they lacked the intrinsic ability to think deeply and refine their reasoning over time. The o1 model, however, integrates this chain of thought process natively, allowing it to perform more complex reasoning tasks with greater accuracy and depth

We have different CoT prompting techniques help AI models improve their reasoning abilities by guiding them through the process of breaking down complex tasks into manageable steps, ultimately leading to more accurate and thoughtful responses. They include:

(i). Few-Shot CoT Prompting

This involves providing the model with a few examples of step-by-step reasoning before asking it to solve a new problem. For instance, if the task is to solve a math problem, the prompt includes a few solved examples that demonstrate the chain of thought process

Example Task: Basic Arithmetic Word Problems

Prompt:

In this example, the model (CHATGPT) is given a few problems with detailed reasoning steps. When presented with a new problem, it follows the same pattern to arrive at the solution.

(ii). Standard CoT Prompting:

This technique is similar to few-shot prompting but focuses specifically on breaking down complex problems into intermediate steps. The model is given examples where each step of the reasoning process is explicitly shown, helping it learn how to approach similar tasks. Okay, lets give chatGpt a prompt.

Example Without Standard CoT Prompting

Example With Standard CoT Prompting

(iii). Zero-Shot Chain-of-Thought (CoT) Prompting

Zero-Shot Chain-of-Thought (CoT) Prompting is a technique used to enhance the reasoning capabilities of large language models by prompting them to generate step-by-step explanations for their answers, even without any prior examples. This method helps the model break down complex tasks into manageable steps, improving accuracy and performance

Example Without Zero-Shot Chain-of-Thought Prompting

Example With Zero-Shot Chain-of-Thought Prompting

2.COT with o1.

You see, you can get the model to output what is called "The chain of Thought", just simply asking the model to think step by step, you can then get much longer output that have reasoning steps within them😉, but guess what, that secrete is already a few years old so, is that what is special about o1😂? Naah!, people thought of 🤔, what about we feed the model with thousands of examples of human step by step reasoning😶... Yeees, that does work, but not really optimal and doesn't scale, So openAi killed it by just training their model to generate their own chain of thought which scale at last.

Again OpenAI’s latest model, o1, significantly enhances reasoning capabilities by integrating Chain-of-Thought (CoT) prompting more deeply than previous models like GPT-4. This enhancement allows the model to break down complex problems into smaller, manageable steps, improving accuracy and performance in tasks such as science, coding, and math. The o1 model spends more time thinking through problems before responding, automatically breaking down tasks into subtasks, which previously required multiple prompts. Additionally, it incorporates a recursive process to reassess its outputs, correcting errors and reducing hallucinations. The model’s advanced safety training enables it to reason about safety rules in context and apply them more effectively, adhering to guidelines more reliably. In challenging benchmark tasks, such as the International Mathematics Olympiad (IMO) and coding competitions, o1 has demonstrated exceptional performance, solving 83% of IMO problems correctly compared to 13% by GPT-4. This integration of CoT reasoning allows o1 to handle more complex tasks with greater accuracy and reliability, marking a significant advancement over previous models.

Question Do you think o1 can actually reaon?

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