diff --git a/README.md b/README.md
index 1868a96..46a232c 100644
--- a/README.md
+++ b/README.md
@@ -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