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docs: add artifact loop engine usage

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README.md
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@ -15,11 +15,11 @@ Engine concepts:
- **`scorer`** — evaluates each iteration and records the outcome.
- **`policy`** — decides what to keep, discard, or try next.
The task spec schema also includes a `mutation` section, but mutation-budget enforcement is reserved for a future baseline-aware orchestration layer and is not yet applied by the current CLI loop.
The task schema and validator include a `mutation` section, but the current CLI loop does not enforce mutation budgets yet because enforcement requires a baseline-aware orchestration layer.
## How it works
The repo is deliberately kept small and only really has three files that matter:
The repo is deliberately kept small. The original training workflow centers on three main files, while the Artifact Loop Engine adds a separate task runner path for editable text artifacts:
- **`prepare.py`** — fixed constants, one-time data prep (downloads training data, trains a BPE tokenizer), and runtime utilities (dataloader, evaluation). Not modified.
- **`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**.
@ -47,12 +47,20 @@ uv run prepare.py
# 4. Manually run a single training experiment (~5 min)
uv run train.py
# 5. Run the Artifact Loop Engine task runner
uv run python scripts/run_task.py --task tasks/skill-quality/task.yaml
```
If the above commands all work ok, your setup is working and you can go into autonomous research mode.
## Artifact Loop Engine
This repository also includes a generic optimization engine for editable text artifacts such as prompts, skills, config files, and small code paths. It uses the same iterate-evaluate-repeat loop as the training workflow, but applies it to task-defined artifacts instead of model code.
Optional sample task command:
```bash
uv run python scripts/run_task.py --task tasks/skill-quality/task.yaml
```
The task runner writes structured iteration results to `work/results.jsonl`.
## Running the agent