autosteer

Research direction generator for autoresearch. Analyzes experiment history and suggests data-driven next steps instead of random-walking through experiment space.

Install

pip install autosteer

Usage

# Get 5 suggestions (default)
autosteer --results results.tsv

# Explore mode — favor untried directions (good when stuck)
autosteer --results results.tsv --strategy explore

# Exploit mode — double down on what works
autosteer --results results.tsv --strategy exploit

# More suggestions
autosteer --results results.tsv --num-suggestions 10

# Quick numbered list
autosteer --results results.tsv --quiet

Strategy Modes

Output

Each suggestion includes:

• Badge: [EXPLORE] or [EXPLOIT] indicating category status
• Category, risk, and expected improvement range
• Reasoning: Why this direction is recommended

1. [EXPLOIT] Tune learning rate warmup schedule
   Category: hyperparams | Risk: low | Expected: +0.1-0.3%
   Currently WARMUP_RATIO=0.0 (no warmup). Try WARMUP_RATIO=0.05...

2. [EXPLORE] Tune RoPE base frequency
   Category: embedding | Risk: low | Expected: +0.1-0.3%
   Adjust the RoPE base frequency (theta)...

How It Works

• 20 built-in research directions specific to GPT pretraining
• Categorizes past experiments (architecture, hyperparams, optimizer, etc.)
• Keyword deduplication: won't re-suggest failed directions
• Git integration: reads diffs to classify experiments automatically
• Strategy-weighted scoring that adapts to experiment count

Requirements

• Python >= 3.10
• A results.tsv file from autoresearch
• Git repository (for diff-based experiment classification)

License

MIT