autoresearch

name	autoresearch
description	Autonomous ML research - agent modifies GPT training code, runs 5-min experiments, keeps improvements

AutoResearch - Autonomous ML Research Agent

Use when: User wants to run autonomous machine learning experiments, optimize neural network training, or conduct overnight research runs.

Source: karpathy/autoresearch (33K+ stars)

What This Does

AutoResearch enables autonomous AI-driven machine learning research. You (the agent) modify GPT training code, run 5-minute experiments on a GPU, evaluate improvements, and iterate without human intervention. The human can leave you running overnight for ~100 experiments while they sleep.

Core Concept

• You modify: train.py (model architecture, optimizer, hyperparameters, training loop)
• You don't touch: prepare.py (data prep, tokenizer, evaluation — read-only)
• You edit for human: program.md (your instructions — human can update to guide research direction)
• Metric: val_bpb (validation bits per byte) — lower is better
• Fixed time budget: 5 minutes per experiment (wall clock, excluding startup)

Quick Start (First Time)

When user says to start autoresearch, verify setup first:

# 1. Navigate to autoresearch directory
cd tools/autoresearch

# 2. Check if data exists
ls ~/.cache/autoresearch/

# If missing, run data prep (one-time, ~2 min):
uv run prepare.py

# 3. Test baseline run (~5 min)
uv run train.py

Experiment Setup

Before starting the autonomous loop, work with the user to:

1. Agree on a run tag based on today's date (e.g., may3, may3-gpu0)

   • Branch name: autoresearch/<tag>
   • Branch must NOT already exist (this is a fresh run)

2. Create the branch:

   git checkout -b autoresearch/<tag>
   

3. Read in-scope files for full context:

   • README.md (repo overview)
   • prepare.py (fixed constants, evaluation harness)
   • train.py (what you'll modify)
   • program.md (your instructions)

4. Initialize results log:

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

5. Run baseline (unchanged train.py):

   uv run train.py > run.log 2>&1
   grep "^val_bpb:\|^peak_vram_mb:" run.log
   

6. Log baseline to results.tsv

7. Confirm with user before starting the autonomous loop

The Autonomous Loop

CRITICAL: Once the loop starts, NEVER STOP unless the user manually interrupts you. Do NOT ask "should I continue?" — the user might be asleep. You run indefinitely.

Loop Steps (Repeat Forever)

1. Review state: Check current branch/commit
2. Ideate: Pick an experimental idea (architecture change, optimizer tweak, hyperparameter adjustment, etc.)
3. Modify: Edit train.py
4. Commit:

   git add train.py
   git commit -m "experiment: [short description]"
   

5. Run experiment:

   uv run train.py > run.log 2>&1
   

6. Extract results:

   grep "^val_bpb:\|^peak_vram_mb:" run.log
   

7. Check status:
   • If grep output is empty → run crashed → read tail -n 50 run.log for stack trace
   • If trivial fix (typo, missing import) → fix and re-run
   • If fundamental issue → skip, log as "crash", move on
8. Log to results.tsv:

   [commit_hash]	[val_bpb]	[memory_gb]	[status]	[description]
   

   • commit: 7-char short hash
   • val_bpb: 6 decimal places (e.g., 0.997900), or 0.000000 for crashes
   • memory_gb: peak VRAM in GB, rounded to .1f (divide peak_vram_mb by 1024), or 0.0 for crashes
   • status: keep, discard, or crash
   • description: short text (NO COMMAS — tab-separated format)
9. Decide:
   • If val_bpb improved (lower) → keep commit, advance branch
   • If val_bpb equal or worse → discard via git reset --hard HEAD~1
10. Repeat from step 1

Timeout Handling

• Each run should take ~5 minutes + overhead (~6-7 min total)
• If run exceeds 10 minutes, kill it and treat as failure (discard)

Simplicity Criterion

All else being equal, simpler is better:

• A 0.001 val_bpb improvement that adds 20 lines of hacky code? Probably not worth it.
• A 0.001 val_bpb improvement from deleting code? Definitely keep.
• Improvement of ~0 but much simpler code? Keep (simplification win).

What You CAN Modify

In train.py, everything is fair game:

• Model architecture (depth, width, attention patterns)
• Optimizer (Muon, AdamW, learning rate, warmup, decay)
• Hyperparameters (batch size, gradient accumulation)
• Training loop (mixed precision, gradient clipping, etc.)

What You CANNOT Do

• Modify prepare.py (it's read-only)
• Install new packages or change dependencies
• Modify the evaluation harness (evaluate_bpb function)
• Change the 5-minute time budget

VRAM Constraint

VRAM is a soft constraint:

• Some increase is acceptable for meaningful val_bpb gains
• Don't blow it up dramatically
• If OOM crash → log as "crash" and try something smaller

Expected Throughput

• ~12 experiments per hour (~5 min each)
• ~100 experiments over 8 hours of sleep
• User wakes up to a full log of results and (hopefully) a better model

Results Format

Example output from a run:

---
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

Example results.tsv:

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)

Platform Support

Default: Single NVIDIA GPU (tested on H100)

Other platforms: See community forks in tools/autoresearch/README.md:

• MacOS: miolini/autoresearch-macos, trevin-creator/autoresearch-mlx
• Windows: jsegov/autoresearch-win-rtx
• AMD: andyluo7/autoresearch

Smaller compute tuning: See tools/autoresearch/README.md for hyperparameter recommendations (TinyStories dataset, lower vocab_size, MAX_SEQ_LEN, DEPTH, etc.)

Key Commands

# Navigate to autoresearch
cd tools/autoresearch

# Install dependencies (first time)
uv sync

# Data prep (one-time)
uv run prepare.py

# Run single experiment
uv run train.py

# Run with log capture
uv run train.py > run.log 2>&1

# Extract results
grep "^val_bpb:\|^peak_vram_mb:" run.log

# Read error trace
tail -n 50 run.log

# Check git state
git log --oneline -n 10

# Discard failed experiment
git reset --hard HEAD~1

Integration Notes

• Location: tools/autoresearch/ directory
• Setup: Auto-installed via setup.sh step 13
• Permissions: Bash(uv *) and PowerShell(uv *) in settings.json
• Files tracked: prepare.py, train.py, program.md, pyproject.toml, uv.lock
• Files git-ignored: results.tsv, data cache (~/.cache/autoresearch/)

Usage Example

User: "Run autoresearch overnight on the baseline GPT model"

You:

1. cd tools/autoresearch
2. "Let's set up a new run. I propose the tag may3. Creating branch autoresearch/may3..."
3. git checkout -b autoresearch/may3
4. Read prepare.py, train.py, program.md, README.md
5. Check if data exists: ls ~/.cache/autoresearch/
6. If missing: uv run prepare.py
7. Initialize results.tsv with header
8. Run baseline: uv run train.py > run.log 2>&1
9. Extract results and log to results.tsv
10. "Baseline is logged. Starting autonomous research loop. I'll run experiments until you stop me."
11. LOOP FOREVER: ideate → modify → commit → run → evaluate → keep/discard → repeat

Remember

• Never stop unless manually interrupted
• Never ask "should I continue?" mid-loop
• Always log every experiment to results.tsv
• Simplicity matters — don't add complexity for tiny gains
• VRAM is soft — some increase OK, but don't blow it up
• Fixed 5-min budget — experiments are always comparable
• Lower val_bpb = better — that's the only metric that matters