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Autoresearch

Autoresearch AI Agent
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Overview

An open-source project that lets AI agents autonomously run LLM training experiments and keep the best model changes.

Autoresearch is an open-source project by Andrej Karpathy that lets AI agents run autonomous machine learning research loops on a small but real LLM training setup. The repository is designed so an agent edits the main training file, launches a fixed 5-minute experiment, evaluates whether the result improved, and then keeps or discards the change before repeating the cycle. Its README describes the setup as a lightweight autonomous research organization driven by instructions in a program.md file rather than traditional manual code iteration. The project is built around a simplified single-GPU nanochat training workflow and is aimed at developers and researchers exploring automated model improvement, agent-driven experimentation, and compact research loops on their own hardware. :contentReference[oaicite:0]{index=0}

Autonomy level

74%

Reasoning: Autoresearch demonstrates high operational autonomy within defined constraints. The AI agent operates independently in a continuous feedback loop, autonomously modifying train.py code, executing 5-minute training runs, and making keep/discard decisions based on validation bits-per-byte (val_bpb) metrics without human intervention. Each experiment i...

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Some of the use cases of Autoresearch:

  • Running autonomous overnight experiments to improve small language model training setups.
  • Testing agent-driven code changes in a controlled single-file training workflow.
  • Exploring automated research loops for architecture, optimizer, and hyperparameter changes.
  • Studying how AI systems can manage iterative machine learning experimentation with minimal human intervention.

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Popularity level: 72%

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