Author(s): Anubhav

Originally published on Towards AI.

The Next AI Breakthrough May Not Be a Bigger Model

In 2019, a researcher named François Chollet built a benchmark called ARC-AGI. The puzzles were simple enough that a child could solve them. They were mostly colored grids where you had to find the pattern and fill in the missing square. He designed them specifically to defeat the one strategy AI labs were betting everything on, which was making the model bigger. For four years, that is what they did, and for four years, ARC-AGI did not move. GPT-3 scored close to zero. GPT-4o scored 5%.

After the later jump achieved by models like o3—improving performance largely by spending more compute (“thinking longer”) at inference—the article argues that this new scaling lever is already showing diminishing returns and can even backfire due to “search degradation,” where longer internal reasoning chains increase chances of self-correction errors and drown useful signals. It then explains how models are taught: post-training via reinforcement learning and reward-based methods can transform a base model into a stronger problem-solver without changing parameter counts, and similar reward-driven recipes have improved coding-agent performance. Finally, the piece points to deeper architectural and systems changes beyond bigger parameters: hybrid transformer/state-space designs and newer attention/caching strategies can reduce the cost of long contexts, making agentic, continuous workloads more feasible—suggesting the next shipped breakthrough will be about training methods and inference/architecture economics rather than just scaling model size.

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Published via Towards AI