Discover how test-time compute and scalable reasoning methods are propelling us toward artificial general intelligence.
Overview
In recent years, breakthroughs in AI, particularly in large language models (LLMs),
have accelerated at a pace that few predicted. Noam Brown, a research scientist at OpenAI,
highlights the shift from traditional scaling (larger datasets and model sizes)
toward more flexible “test-time compute” — giving models additional reasoning steps at inference.
This approach, demonstrated with models like O1, shows remarkable improvements in problem-solving.
Key Insights:
Scaling test-time compute unlocks complex reasoning capabilities without linear cost increases.
Future research hurdles are likely less challenging than those already overcome.
Models that can self-correct, strategize, and break down problems signal a move toward general intelligence.
Techniques that scale with more data and compute will outlast brittle, domain-specific solutions.
Industry Impact
The implications of these advancements are vast. As LLMs become more adept at reasoning and problem-solving:
They can surpass human experts in specialized domains, accelerating research and innovation.
They open avenues for cost-effective, complex computations where traditional scaling is prohibitive.
They reduce reliance on manual scaffolding, enabling more automated and robust solutions.
Enterprises can leverage powerful, general-purpose models augmented by specialized tools to handle diverse tasks.
This transformation encourages the AI community to invest in test-time strategies, multi-step reasoning,
and technologies that scale gracefully with resources, ultimately pushing us closer to true AGI.
Comparing Model Capabilities
Below is a simplified table illustrating how different generations of AI models
have improved over time in key metrics like reasoning depth, cost-efficiency, and domain generality.
Generation
Reasoning Depth
Cost per Query
Domain Coverage
Example Models
Early LLMs (Pre-2020)
Shallow, surface-level reasoning
Low
Narrow (text completion, basic Q&A)
GPT-2
Mid-Era LLMs (2020-2022)
Moderate reasoning with prompt tricks
Moderate
Wider (code, math, logic with tuning)
GPT-3, Codex
Advanced LLMs (2023-Present)
Deeper, multi-step reasoning via test-time compute
Flexible (pay more only when needed)
Broad, multi-domain tasks, research-level problem solving
O1, GPT-4 (with reasoning steps)
Using the OpenAI API
OpenAI’s API allows developers to interact with models like GPT-4 or O1 (once available)
to generate text, solve problems, and power sophisticated applications. Here’s a simple example
in JavaScript using the Fetch API. Make sure to replace YOUR_API_KEY
with your actual OpenAI API key.
// Example: Using OpenAI API with fetch in JavaScript
const API_KEY = "YOUR_API_KEY";
async function getCompletion(prompt) {
const response = await fetch("https://api.openai.com/v1/chat/completions", {
method: "POST",
headers: {
"Content-Type": "application/json",
"Authorization": `Bearer ${API_KEY}`
},
body: JSON.stringify({
model: "gpt-4",
messages: [{ role: "user", content: prompt }],
max_tokens: 100,
temperature: 0.7
})
});
const data = await response.json();
console.log(data.choices[0].message.content);
}
getCompletion("Explain how test-time compute works in simple terms.");
Watch the Discussion
Dive deeper into the conversation with Noam Brown, exploring how O1’s release sets the stage for the path to AGI
and reshapes our understanding of scalable intelligence.