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docs(guideline): make SearchQA the first demo — copy-paste materialization snippet + train command

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Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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@@ -445,30 +445,48 @@ skillopt/ <span class="tok-c"># the package</span>
<p><strong>What ships in this repo:</strong> ready-to-use configs and
pretrained skills (<code>ckpt/</code>) for six benchmarks, plus
lightweight <em>ID manifests</em> under <code>data/</code>. The manifests
list which examples each split uses but do <strong>not</strong> contain
the example contents — so for most benchmarks you materialize the data
once before training (see below).</p>
<p><strong>Fastest out-of-the-box run — ALFWorld.</strong> Its bundled
split (<code>data/alfworld_path_split</code>) is directly usable; you
only need the ALFWorld game files:</p>
<pre><code><span class="tok-k">pip</span> install -e <span class="tok-s">".[alfworld]"</span>
<span class="tok-k">alfworld-download</span>
<span class="tok-k">export</span> ALFWORLD_DATA=~/.cache/alfworld <span class="tok-c"># data root containing json_2.1.1</span>
pin exactly which examples each split uses but do <strong>not</strong>
contain the example contents — so you materialize the data once before
the first run.</p>
<p><strong>Step 1 — materialize the SearchQA splits</strong> (one-time; downloads the ~6.5&nbsp;GB source dataset). The manifest IDs match the <code>key</code> field of the
<a href="https://huggingface.co/datasets/lucadiliello/searchqa">lucadiliello/searchqa</a>
dataset:</p>
<pre><code><span class="tok-k">pip</span> install datasets
<span class="tok-k">python</span> - &lt;&lt;'PY'
import json, os
from datasets import load_dataset
<span class="tok-k">python</span> scripts/train.py \
--config configs/alfworld/default.yaml \
--split_dir data/alfworld_path_split \
ds = load_dataset("lucadiliello/searchqa")
by_key = {r["key"]: r for split in ds.values() for r in split}
for split in ["train", "val", "test"]:
ids = json.load(open(f"data/searchqa_id_split/{split}/items.json"))
items = []
for x in ids:
r = by_key[x["id"]]
items.append({"id": r["key"], "question": r["question"],
"context": r["context"], "answers": r["answers"]})
os.makedirs(f"data/searchqa_split/{split}", exist_ok=True)
json.dump(items, open(f"data/searchqa_split/{split}/items.json", "w"))
print(split, len(items))
PY</code></pre>
<p><strong>Step 2 — train</strong> (4 epochs &times; batch 40; see §3.2
for the CLI reference):</p>
<pre><code><span class="tok-k">python</span> scripts/train.py \
--config configs/searchqa/default.yaml \
--split_dir data/searchqa_split \
--azure_openai_endpoint https://your-resource.openai.azure.com/ \
--optimizer_model gpt-5.5 \
--target_model gpt-5.5</code></pre>
<p><strong>Other benchmarks (e.g. SearchQA)</strong> require a one-time
data materialization step: download the raw dataset from the source
listed in <a href="https://github.com/microsoft/SkillOpt/blob/main/data/README.md"><code>data/README.md</code></a>,
match the manifest IDs to raw examples (the README documents the lookup
key per benchmark), and write the resulting
<code>train/val/test</code> item files into a split directory. Then run
the commands in §3.2 with <code>--split_dir</code> pointing at it. The
required item fields are documented in §4.2.</p>
<p>Other benchmarks follow the same pattern — materialize from the raw
source listed in
<a href="https://github.com/microsoft/SkillOpt/blob/main/data/README.md"><code>data/README.md</code></a>
(it documents the lookup key per benchmark), then point
<code>--split_dir</code> at the result. The one exception is
<strong>ALFWorld</strong>, whose bundled
<code>data/alfworld_path_split</code> works directly: just
<code>pip install -e ".[alfworld]" &amp;&amp; alfworld-download</code> and
set <code>$ALFWORLD_DATA</code>.</p>
<p>To sanity-check your setup <em>without</em> training, evaluate a
packaged pretrained skill instead (§3.3 uses
<code>ckpt/searchqa/gpt5.5_skill.md</code>), or launch the monitoring