State of AI in 4 plots.

The 200 ELO points difference between recent models and a model that is 2 years old means that a human rater has ~75% chance of preferring an answer from a recent model.

Based on available data, all indicators about the progress of AI (in particular LLMs) remain strong.

My co-authors: Jacob Kooi and Zhao Yang
Paper: arxiv.org/abs/2505.15345
Codebase: github.com/Jacobkooi/Ha...

Directly implementing the Hadamax encoder in other algorithms such as C51 also shows over 60% improvements.

The Hadamax architecture can be implemented in any pixel-based encoder. The most important design choices are:

1. Convolutional Hadamard Representations.
2. Max-pooling instead of convolutional down-sampling.
3. Gaussian Error Linear Unit activations.

Without changing any algorithmic hyperparameters, this encoder substitution places Hadamax-PQN among state-of-the-art model-free reinforcement learning, while remaining an order of magnitude faster than Rainbow.

📢New paper on arXiv: Hadamax Encoding: Elevating Performance in Model-Free Atari. (arxiv.org/abs/2505.15345)

Our Hadamax (Hadamard max-pooling) encoder architecture improves the recent PQN algorithm’s Atari performance by 80%, allowing it to significantly surpass Rainbow-DQN!