AI BriefingFirst Steps Toward Automated AI Research
Key point
Recursive unveiled early results from an automated AI research system that achieved SOTA in model training and GPU kernel optimization.
Details
Recursive developed a system that automates the research loop, from idea proposal to implementation, experimentation, validation, and selecting the next experiment. This system was designed with the goal of recursively self-improving AI that enhances its own performance by leveraging open-ended algorithmic principles.
The system achieved SOTA (State-of-the-art) results on three key benchmarks:
- NanoChat Autoresearch: When training a small language model within a fixed compute budget, it reached the same loss 1.3x faster than existing community results
- NanoGPT Speedrun: Reduced training time to reach a specific performance level by 2.2 seconds
- SOL-ExecBench: Reduced the gap to optimal performance by 18% in GPU kernel optimization tasks
Notably, in the NanoChat test, the system outperformed the results of the autoresearch@home community, where humans and agents collaborated, demonstrating that significant performance improvements are possible even when starting from a very basic model (Vanilla Transformer).
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