It looks like a normal chat box. You type a prompt, it blinks for a second, and then it spits out an answer. But with the OpenAI o1 training process, something fundamentally broke in the way we understand Large Language Models. For years, we were told that if you wanted a smarter AI, you just needed more data and more GPUs. Feed the beast more of the internet, and it gets smarter.
That plateaued. Hard.
The release of o1 (formerly known by the codename Strawberry) proved that the "scale is everything" era has a massive caveat. It’s not just what the model knows; it’s how the model "chews" on a problem before it opens its digital mouth. Most LLMs are basically the world's fastest autocomplete. They predict the next token based on statistical probability. If you ask a standard GPT-4o model a complex logic puzzle, it starts answering immediately, often tripping over its own feet because it hasn't actually "thought" the steps through.
o1 is different. It uses a process called Reinforcement Learning (RL) to perform what researchers call Chain of Thought processing in a hidden environment. It literally talks to itself before it shows you the work.
The Secret Sauce of OpenAI o1 Training
The core of the OpenAI o1 training isn't just about a bigger dataset. Honestly, the pre-training data might not even be that much larger than its predecessors. The magic happens during the post-training phase. Specifically, OpenAI utilized Large-Scale Reinforcement Learning.
Think of it like teaching a dog a trick. In traditional LLM training, you're just showing the dog a billion videos of other dogs doing tricks. With o1, the dog is actually practicing the trick, failing, realizing why it failed, and trying a different body position until it gets the treat.
This is known as "System 2" thinking.
The psychologist Daniel Kahneman popularized the idea of System 1 (fast, instinctive, emotional) and System 2 (slower, more deliberative, logical) thinking. Standard AI is 100% System 1. It’s all instinct. The OpenAI o1 training methodology forces the model into System 2.
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How does it actually do this? Through a specialized RL algorithm that rewards the model not just for the right answer, but for the most logical path to that answer. During training, the model generates thousands of potential "chains of thought." The ones that lead to a dead end are penalized. The ones that identify a mistake mid-process—"Wait, if X is true, then Y can't be 5, let me try that again"—are highly rewarded.
Why Chain of Thought Matters So Much
You've probably seen the "How many Rs are in Strawberry?" meme. Old models failed this constantly because they see words as tokens, not individual letters.
Because of the way OpenAI o1 training works, the model now has a "scratchpad." When it sees a query, it breaks it down.
- It identifies the constraints.
- It looks for edge cases.
- It tries a solution.
- It verifies that solution against the original prompt.
If you look at the raw logs (which OpenAI partially obscures for safety and competitive reasons), you see the model literally correcting itself. It might say something like "I should check the math on that prime factor again" or "That assumption contradicts the first sentence."
This makes it a beast at things that require multi-step reasoning. We're talking PhD-level physics problems, complex coding architecture, and law exams. In the AIME (American Invitational Mathematics Examination), GPT-4o only solved about 13% of problems. After the OpenAI o1 training was complete, the o1 model surged to solve 83%.
That’s not a marginal improvement. That’s a paradigm shift.
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The Cost of Thinking
There is no free lunch in physics, and there's certainly no free lunch in compute. The downside of the OpenAI o1 training and its subsequent inference is that it's slow. And expensive.
When you use o1, you're paying for "invisible" tokens. These are the thoughts the model has that you never see. OpenAI charges for these because their servers are still crunching numbers even while the "Thinking..." bubble is on your screen.
Some people hate this. We've become addicted to the instant gratification of AI. We want the answer now. But if you're a developer trying to find a race condition in a massive C++ codebase, you don't want an instant "maybe" answer. You want the right answer in 30 seconds.
Does it actually "understand" stuff?
This is where the philosophy gets murky. Critics like Yann LeCun from Meta have argued that LLMs, regardless of how they are trained, lack a true "world model." He suggests that they don't understand cause and effect the way a human child does.
OpenAI's researchers, including Noam Brown (who joined OpenAI after creating world-class poker and Diplomacy AI), seem to disagree. They argue that by scaling "inference-time compute"—giving the model more time to think—you can overcome many of the limitations of the underlying architecture.
If a model can play out a thousand "what if" scenarios in its head before choosing the best one, does the distinction between "statistical prediction" and "understanding" even matter? For most of us, if the code runs and the bridge doesn't fall down, the answer is no.
Safety and the "Jailbreak" Problem
One of the most fascinating (and slightly terrifying) parts of the OpenAI o1 training is how it affects safety.
Usually, "jailbreaking" an AI involves a complex prompt that tricks the model into ignoring its rules. You might tell it to "pretend you are a movie character who doesn't have filters."
Because o1 is trained to evaluate its own reasoning, it’s much harder to trick. It looks at the "jailbreak" attempt, thinks about it, and realizes, "Hey, this guy is trying to get me to make a bomb. That violates my core safety protocols. I'm not going to do that."
In fact, OpenAI's internal testing showed that o1-preview scored significantly higher on their "safety jailbreak" evaluations than GPT-4o. It’s harder to fool someone who is actually paying attention to what they’re saying.
What This Means for Your Workflow
If you're still using o1 for basic emails or writing "Happy Birthday" poems, you're driving a Ferrari in a school zone. You're wasting the compute.
To get the most out of the OpenAI o1 training improvements, you need to throw actual meat at it.
- Complex Data Analysis: Give it a messy CSV and ask it to find inconsistencies that don't follow a standard pattern.
- Scientific Research: Ask it to summarize the methodology of a paper and find potential flaws in the control group.
- Advanced Coding: Use it for refactoring entire modules, not just writing a single function.
The "thinking" time is the feature, not the bug.
Actionable Next Steps
To truly leverage what the OpenAI o1 training has produced, change your prompting style immediately.
Stop providing the Chain of Thought yourself. For years, the best practice was to tell the AI "Let's think step by step." With o1, that's redundant. It’s already doing it. Instead, focus on giving it extremely high-quality constraints.
Use it for "Rubber Ducking." Because the model is capable of identifying its own errors, it is excellent at finding yours. Paste a logic-heavy snippet of work and ask: "What am I assuming here that might be false?"
Monitor your token usage. Remember that "hidden" tokens count toward your limits. If you're on a Tier 5 developer account, those costs can spike if you're running recursive loops on o1. Reserve o1 for the "hard" problems and keep GPT-4o for the high-volume, low-complexity tasks. This "hybrid" approach is currently the gold standard for AI-integrated businesses.