How OpenAI’s AI Reasoning Models Are Pushing the Limits of Intelligent Agents

The Rise of OpenAI's AI Reasoning Models

When Hunter Lightman joined OpenAI in 2022, ChatGPT was already making waves as one of the fastest-growing products ever launched. While that buzz unfolded, Lightman focused quietly on training AI models to solve high school math competitions. His team's work, known as MathGen, has become key to OpenAI's leading efforts in AI reasoning models—the technology enabling AI agents to perform computer tasks like humans do.

“We aimed to improve mathematical reasoning, where models initially struggled,” Lightman said. Although today's AI systems still make errors and face challenges with complex tasks, they've made significant progress. One model even earned a gold medal at the International Math Olympiad, a competition for top high school mathematicians worldwide.

OpenAI expects these reasoning improvements to extend beyond math, powering general-purpose AI agents that can handle a variety of tasks. Unlike ChatGPT, which emerged somewhat unexpectedly, these agents result from years of focused research. OpenAI CEO Sam Altman described a future where users simply ask their computer for what they need, and it completes tasks autonomously—an approach often referred to as AI agents with vast potential benefits.

The Reinforcement Learning Renaissance

The advances in reasoning models and agents are closely linked to reinforcement learning (RL), a method that teaches AI through feedback on its decisions in simulated settings. RL has been around for decades; a notable example is Google DeepMind’s AlphaGo, which defeated a world champion Go player in 2016.

Early on, OpenAI researchers like Andrej Karpathy explored using RL to build AI agents capable of operating computers. However, it took years to develop the right models and training methods. By 2018, OpenAI introduced its first large language model (LLM) in the GPT series, trained on massive internet data. These models excelled at text but struggled with basic math.

Breakthrough came in 2023 with a model initially named “Q*” and later “Strawberry.” This model combined LLMs, RL, and a method called test-time computation, which gives the AI more time and resources to think through problems and verify each step before answering. This approach introduced “chain-of-thought” (CoT) reasoning, markedly improving AI performance on unseen math problems.

El Kishky, involved in this work, noted the model seemed to reason, noticing mistakes and correcting itself—almost like reading human thought processes. By integrating these techniques, OpenAI created Strawberry, which paved the way for the o1 reasoning model. Recognizing the planning and fact-checking strengths of these models, the company saw their potential for powering AI agents.

Scaling Reasoning

OpenAI identified two main ways to enhance AI reasoning models: increasing computational power during post-training and allowing models more processing time when answering questions. Lightman highlighted how OpenAI focuses on future scaling, not just current capabilities.

Following the Strawberry breakthrough, OpenAI established an “Agents” team led by Daniel Selsam to push this new paradigm further. Initially, the distinction between reasoning models and agents wasn’t clear—they simply aimed to create AI capable of complex task completion.

This work merged into the development of the o1 reasoning model, with leadership from OpenAI co-founder Ilya Sutskever and others. Building o1 required significant resources, especially talent and GPUs. At OpenAI, research is bottom-up; proving breakthroughs secured the necessary support.

The startup’s mission to develop artificial general intelligence (AGI) helped prioritize these efforts. By focusing on creating the smartest AI models rather than immediate products, OpenAI could invest heavily in o1. In contrast, some competing labs faced limits in resource allocation for such projects.

By late 2024, many AI labs noticed diminishing returns from traditional scaling of pretrained models. Today, advances in reasoning models are a major driver of progress across the AI field.

The Next Frontier: AI Agents for Subjective Tasks

Current AI agents excel in clear-cut areas like coding. OpenAI’s Codex aids software engineers with routine programming tasks, while Anthropic’s models power popular AI coding tools such as Cursor and Claude Code—early examples of paid AI agent services.

However, general-purpose agents like OpenAI’s ChatGPT Agent and Perplexity’s Comet still struggle with complex, subjective tasks such as online shopping or finding parking. They often take longer than desired and make avoidable errors. While these systems are early versions, improving them requires training models to handle less verifiable, more nuanced tasks.

Lightman refers to this challenge as a data problem. Current research focuses on enabling AI to learn from tasks without clear-cut right answers. OpenAI has developed new general-purpose RL techniques to teach these skills, demonstrated by their IMO gold medal-winning model. This model spawns multiple agents to explore ideas simultaneously before selecting the best solution.

Similar approaches are gaining traction at Google and xAI, with state-of-the-art models applying these methods. Noam Brown from OpenAI predicts that these models will improve not just in math but broader reasoning areas, with rapid progress expected to continue.

These advances may be evident in the upcoming GPT-5 model, which OpenAI hopes will outperform competitors and power more capable agents for both developers and consumers. The company also aims to simplify user experience, creating agents that intuitively grasp user needs without complicated settings.

El Kishky envisions AI agents that know when to activate specific tools and how long to reason—building toward an ultimate version of ChatGPT that can take care of any task online in a way that matches user preferences. This vision marks a significant shift from today’s ChatGPT, and OpenAI’s research is firmly moving in this direction.

While OpenAI once led the field decisively, it now faces strong competition from Google, Anthropic, xAI, and Meta. The key question is whether OpenAI can deliver this agent-based future before its rivals do.