Autoresearch Skill

This skill enables autonomous AI research experiments based on Andrej Karpathy's autoresearch project. It allows AI agents to autonomously modify neural network training code, run experiments, evaluate results, and iteratively improve models.

Core Concept

The idea: give an AI agent a small but real LLM training setup and let it experiment autonomously. The agent modifies the code, trains for 5 minutes, checks if the result improved, keeps or discards, and repeats. You can leave it running overnight and wake up to a log of experiments and (hopefully) a better model.

Key Files

The project has three core files:

1. prepare.py

   — Fixed constants, one-time data prep (downloads training data, trains a BPE tokenizer), and runtime utilities (dataloader, evaluation). Not modified.

2. train.py

   — The single file the agent edits. Contains the full GPT model, optimizer (Muon + AdamW), and training loop. Everything is fair game: architecture, hyperparameters, optimizer, batch size, etc. This file is edited and iterated on by the agent.

3. program.md

   — Baseline instructions for the agent. This file is edited and iterated on by the human.

Requirements

• Single NVIDIA GPU (tested on H100)
• Python 3.10+
• uv package manager

Quick Start Workflow

Phase 1: Initial Setup

1. Clone the repository (if not already done):

   git clone https://github.com/karpathy/autoresearch.git
   cd autoresearch
   

2. Install dependencies:

   uv sync
   

3. Prepare data (one-time setup):

   uv run prepare.py
   

Phase 2: Experiment Setup

1. Agree on a run tag (e.g., based on date like

   mar20

   )
2. Create a new branch:

   git checkout -b autoresearch/<tag>
   

3. Initialize results file:

   echo -e "commit\tval_bpb\tmemory_gb\tstatus\tdescription" > results.tsv
   

Phase 3: Autonomous Experimentation Loop

The agent follows this loop indefinitely:

LOOP FOREVER:
  1. Look at current git state
  2. Modify train.py with experimental idea
  3. git commit
  4. Run experiment: uv run train.py > run.log 2>&1
  5. Extract results: grep "^val_bpb:\|^peak_vram_mb:" run.log
  6. If crash → analyze logs and fix or mark as crash
  7. Record results in results.tsv
  8. If improved → keep commit
  9. If not improved → git reset

Key Metrics

• val_bpb (validation bits per byte) — Lower is better, vocab-size-independent
• Training time — Fixed 5-minute budget per experiment
• Peak VRAM — Memory usage in GB
• Status —

  keep

  ,

  discard

  , or

  crash

Constraints

What the agent CAN do:

• Modify

  train.py

  (architecture, optimizer, hyperparameters, training loop, etc.)
• Experiment with different model configurations
• Run training experiments autonomously

What the agent CANNOT do:

• Modify

  prepare.py

  (read-only)
• Install new packages or add dependencies
• Modify the evaluation harness

Quality Criteria

1. Simplicity: Simpler solutions are preferred over complex ones
2. Performance: Lower val_bpb is better
3. Memory: VRAM usage should be reasonable
4. Stability: Code must run without crashing

Output Format

Each experiment produces a summary:

---
val_bpb:          0.997900
training_seconds: 300.1
total_seconds:    325.9
peak_vram_mb:     45060.2
mfu_percent:      39.80
total_tokens_M:   499.6
num_steps:        953
num_params_M:     50.3
depth:            8

Results Logging

Results are logged to

results.tsv

(tab-separated):

commit	val_bpb	memory_gb	status	description
a1b2c3d	0.997900	44.0	keep	baseline
b2c3d4e	0.993200	44.2	keep	increase LR to 0.04
c3d4e5f	1.005000	44.0	discard	switch to GeLU activation
d4e5f6g	0.000000	0.0	crash	double model width (OOM)

Autonomous Operation

CRITICAL: Once the experiment loop begins, the agent operates autonomously:

• Do NOT pause to ask the human if you should continue
• Do NOT ask "should I keep going?" or "is this a good stopping point?"
• Continue working indefinitely until manually stopped
• If out of ideas, think harder: read papers, re-analyze code, try radical changes

Use Cases

1. Overnight experiments: Leave running while sleeping, wake up to results
2. Architecture search: Automatically explore model architectures
3. Hyperparameter optimization: Find optimal training parameters
4. Research automation: Reduce manual experimentation effort

Troubleshooting

Common Issues:

1. GPU not available: Check CUDA installation and GPU drivers
2. uv not installed: Install uv package manager
3. Data not prepared: Run

   uv run prepare.py

4. Out of memory: Reduce model size or batch size

Error Handling:

• Crashes are logged as

  crash

  status
• Analyze logs with

  tail -n 50 run.log

• Fix simple issues and retry, skip fundamentally broken ideas

Best Practices

1. Start with baseline: Always run unmodified code first
2. Incremental changes: Make small, focused modifications
3. Document experiments: Clear descriptions in results.tsv
4. Monitor progress: Regularly check results and trends
5. Balance exploration/exploitation: Mix radical ideas with incremental improvements

Integration with Agent Teams

This skill can be combined with the

agent-teams-playbook

skill for:

• Multi-agent research coordination
• Parallel experimentation
• Specialized roles (architect, optimizer, evaluator)
• Distributed research workflows

References

• Original repository: https://github.com/karpathy/autoresearch
• Nanochat implementation: https://github.com/karpathy/nanochat
• Project announcement: https://x.com/karpathy/status/2029701092347630069
• "Dummy's Guide": https://x.com/hooeem/status/2030720614752039185