AI R&D runs on a cycle of hypothesis, experiment, and analysis — each step demanding substantial manual engineering effort. A new framework from researchers at SII-GAIR aims to close that bottleneck by automating the full optimization loop for training data, model architectures, and learning algorithms. A new framework called ASI-EVOLVE, developed by researchers at the Generative Artificial Intelligence Research Lab (SII-GAIR), aims to solve this bottleneck. Designed as an agentic system for AI-for-AI research, it uses a continuous “learn-design-experiment-analyze” cycle to automate the optimization of the foundational AI stack. In experiments, this self-improvement loop autonomously discovered novel designs that significantly outperformed state-of-the-art human baselines. The system generated novel language model architectures, improved pretraining data pipelines to boost benchmark scores by over 18 points, and designed highly efficient reinforcement learning algorithms.  For enterprise teams running repeated optimization cycles on their AI systems, the framework offers a path to reducing manual engineering overhead while matching or exceeding the performance of human-designed baselines. The data and design bottleneck Engineering teams can only explore a tiny fraction of the …