autoresearch
AutoResearch
Autonomously run an indefinite ML research loop: modify training code, run a timed experiment, measure one metric, keep improvements, discard regressions — repeat until manually stopped. Inspired by Karpathy's autoresearch and extended with cross-platform support (CUDA / Apple Silicon MPS / CPU / Windows) and user-configurable experiment parameters.
Overview
AutoResearch turns an AI agent into a tireless overnight researcher. Given a training codebase and a metric to minimize (or maximize), the agent continuously proposes code modifications, runs fixed-budget training runs, logs results, and builds a growing record of what works — without requiring human involvement between iterations.
The core loop:
┌──────────────────────────────────────────────────────────────────┐
│ AUTONOMOUS RESEARCH LOOP │
│ │
│ [1] Read current [2] Propose [3] Apply change │
│ codebase → modification → to train.py │
│ ↑ ↓ │
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