Usman Salis
Recent analysis of OpenAI's public benchmark data has revealed a critical insight that challenges how we think about AI performance improvement. The data shows that reinforcement learning — the training method behind many recent AI advances — scales far less efficiently than inference-based approaches. New evidence suggests that optimizing how models think during output generation delivers better results with dramatically less computational cost.
Trader Toby Ord Highlights Efficiency Gap in OpenAI Data
Toby Ord, a senior research fellow at Oxford University and AI ethics scholar, recently highlighted a striking pattern in OpenAI's AIME benchmark results. His analysis shows that reinforcement learning demanded a 10,000× compute increase to match the progress that inference scaling achieved with just 100×.
The data plots computational resources against Pass-at-1 performance, measuring how frequently models solve complex reasoning problems correctly. OpenAI's o1 and o3 models illustrate this divide: o1 represents RL-heavy training while o3 showcases inference-driven scaling. The difference is dramatic — o3's performance rises sharply, whereas o1 required enormous computational power to reach each benchmark level.
The Shift from Training Power to Thinking Efficiency
Reinforcement learning from human feedback has driven AI improvements for years, but it demands exponentially more resources for each performance boost. Inference scaling works differently — it enhances performance during output generation rather than pushing training limits. This allows significant capability growth without proportional increases in hardware or energy costs, focusing on improving real-time reasoning rather than endlessly expanding training compute.
Why Efficiency Now Defines Competitive Advantage
- Lower costs and faster iteration make development more accessible
- Mid-sized teams could achieve advanced results without massive infrastructure
- Rapid capability expansion may outpace current safety frameworks
- The competitive edge shifts from biggest training clusters to smartest inference systems
OpenAI's data points toward a future where breakthrough progress depends less on raw computational power and more on architectural intelligence. The company's o1 and o3 models reflect this direction, prioritizing reasoning optimization over parameter expansion.
From Brute Force to Cognitive Design
For nearly a decade, AI progress meant bigger datasets and larger models. With inference scaling outperforming reinforcement learning in efficiency, the next frontier prioritizes teaching models to reason better rather than training them longer. This shifts emphasis from accumulating training data to refining cognitive architecture.
Usman Salis
