AI Research Experiment
Summary
This content details an experiment using AI agents for training optimization and model compression, showing that tight scoping prevents task drift. The authors open-sourced their bash-wrapped Codex loop and techniques for compressing a 2.5 TB model to fit consumer GPUs.
Highlights
- Autoresearch agents require tight scoping to avoid drifting into unrelated tasks.
- The authors open-sourced a bash-wrapped Codex loop enforcing A/B testing.
- Experiment 2 compressed a 2.5 TB Kimi-k2.5 model to fit on 8x RTX 3090s.
- The bottleneck for autonomous AI research is infrastructure, not intelligence.
- Testing different LLMs revealed varying behaviors in autonomous research loops.
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Context
Audience
Machine Learning Engineers and AI Researchers
DomainMachine Learning
Formattechnical blog post
Accessfree online
Topics
Related
Karpathy's nanochat frameworkCodexKimi-k2.5RTX 3090Model CompressionTraining Optimization