For OpenAI, o1 represents a step toward its broader goal of human-like artificial intelligence. More practically, it does a better job at writing code and solving multistep problems than previous models. But it’s also more expensive and slower to use than GPT-4o. OpenAI is calling this release of o1 a “preview” to emphasize how nascent it is.

The training behind o1 is fundamentally different from its predecessors, OpenAI’s research lead, Jerry Tworek, tells me, though the company is being vague about the exact details. He says o1 “has been trained using a completely new optimization algorithm and a new training dataset specifically tailored for it.”

OpenAI taught previous GPT models to mimic patterns from its training data. With o1, it trained the model to solve problems on its own using a technique known as reinforcement learning, which teaches the system through rewards and penalties. It then uses a “chain of thought” to process queries, similarly to how humans process problems by going through them step-by-step.

At the same time, o1 is not as capable as GPT-4o in a lot of areas. It doesn’t do as well on factual knowledge about the world. It also doesn’t have the ability to browse the web or process files and images. Still, the company believes it represents a brand-new class of capabilities. It was named o1 to indicate “resetting the counter back to 1.”

I think this is the most important part (emphasis mine):

As a result of this new training methodology, OpenAI says the model should be more accurate. “We have noticed that this model hallucinates less,” Tworek says. But the problem still persists. “We can’t say we solved hallucinations.”

  • @khepri
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    243 months ago

    So they slapped some reinforcement learning on top of their LLM and are claiming that gives it “reasoning capabilities”? Or am I missing something?

    • @Evotech
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      143 months ago

      It’s like 3 lms on top of eachother in a trenchcoat, and appau a calculator so it gets math right

    • Zos_Kia
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      33 months ago

      No the article is badly worded. Earlier models already have reasoning skills with some rudimentary CoT, but they leaned more heavily into it for this model.

      My guess is they didn’t train it on the 10 trillion words corpus (which is expensive and has diminishing returns) but rather a heavily curated RLHF dataset.