Skip to main content

Documentation Index

Fetch the complete documentation index at: https://docs.10et.ai/llms.txt

Use this file to discover all available pages before exploring further.

Train a lightweight RL model on your project’s (state, action, reward) tuples. The trained policy biases agents toward actions that historically improved YOUR metrics.

​
Usage

tenet train                    # Train on all accumulated tuples
tenet train --dry-run          # Show what would be trained without running

​
How It Works

  1. Reads .tenet/training-buffer.jsonl — tuples from agent rounds
  2. Extracts features: action type, files affected, scope, prior score
  3. Trains a small neural network to predict reward from state+action
  4. Saves weights to .tenet/policy-weights.json
  5. Future agent runs consult the policy head before choosing actions

​
When to Train

  • After accumulating 50+ tuples (minimum for useful signal)
  • After a batch of overnight agent runs
  • The overnight build script runs training automatically

​
See Also