how this tiny model beat ChatGPT on the “AGI” benchmark [HRM & TRM]

Two novel AI models, HRM (27M parameters) and TRM (7M parameters), challenge the scaling paradigm by outperforming large language models like GPT-4 on the ARC AGI benchmark through recursive reasoning. Instead of processing everything in one pass, these tiny models iteratively refine answers using dual-network architectures with fast and slow update cycles. TRM achieves 40% on ARC AGI with just 7 million parameters by training on actual loop behavior rather than assumed equilibrium states. Empirical results show that smaller models with more recursion outperform larger models with more layers, suggesting that for constrained logical tasks, iterative refinement beats raw parameter scaling.