Analysis Suggests Progress in Reasoning AI Models May Soon Stall

A report from Epoch AI, a nonprofit research organization, warns that rapid progress in reasoning-focused AI models may soon hit a plateau. The analysis suggests that within the next year, performance gains from these models could begin to slow.

Reasoning models—such as OpenAI’s o3—have recently shown strong improvements on AI benchmarks, particularly in areas like math and programming. These models excel by applying more computational effort to complex problems, though this typically results in slower task completion compared to traditional models.

The development of reasoning models involves first training a standard model on large datasets, followed by reinforcement learning—a process that provides feedback to help the model improve its problem-solving.

Reinforcement Learning Gets a Major Boost in Computing Power

Until now, AI labs like OpenAI haven’t heavily invested computing resources into the reinforcement learning phase. That’s beginning to change. OpenAI reportedly used around 10 times more computing power to train its o3 model compared to o1, with Epoch speculating that the bulk of this increase was dedicated to reinforcement learning. OpenAI researcher Dan Roberts also noted the company intends to emphasize reinforcement learning even more in future training efforts.

However, according to Epoch, there is a ceiling to how much computing can realistically be used in reinforcement learning, which could limit future gains.

Reinforcement Learning Advances Rapidly, But May Soon Align with Overall AI Progress

Josh You, an analyst at Epoch and author of the report, notes that performance improvements from conventional AI training are currently increasing fourfold each year, whereas advancements from reinforcement learning are accelerating tenfold every three to five months. He adds that by around 2026, progress in reasoning-focused training is likely to align with the broader pace of AI development.

Epoch’s analysis relies on several assumptions and incorporates public statements from AI industry leaders. However, it also argues that scaling reasoning models may face obstacles beyond computing power—particularly due to the significant research overhead involved.

“You” notes, “If ongoing research requires consistently high overhead, the scalability of reasoning models could fall short of expectations.” He adds that rapid increases in available computing power are likely a key driver of reasoning model progress, making it an important trend to monitor.

The possibility that reasoning models could soon hit a performance ceiling may be concerning for the AI industry, which has heavily invested in developing these systems. Research has already highlighted major drawbacks, including high operating costs and a greater tendency to generate false or “hallucinated” information compared to some traditional models.

Read the original article on: TechCrunch

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