Your First Experiment

This guide walks through running a complete SkillOpt training on SearchQA.

1. Choose a Benchmark

SkillOpt includes ready-to-use configs for several benchmarks. End-to-end runtime depends on the chosen models, provider latency, worker limits, and dataset size, so the project does not promise fixed wall-clock estimates.

Benchmark	Modality	Additional setup
SearchQA	Text QA	Materialize the released ID manifest
DocVQA	Document/image QA	Obtain and materialize images and examples
ALFWorld	Embodied agent	Install ALFWorld and download its assets

We'll use SearchQA because it is the simplest text-only walkthrough.

2. Install and Materialize SearchQA

The repository contains a stable SearchQA ID manifest, not the full runnable examples. From a source checkout, install the data extra and materialize the split once:

python -m pip install -e ".[searchqa]"
python scripts/materialize_searchqa.py

By default, the materializer reads data/searchqa_id_split/ and writes the train/validation/test payloads expected by the config to data/searchqa_split/; both paths have command-line overrides.

3. Configure

Configure and export one model backend as described in Installation. For example:

cp .env.example .env
# Edit .env, choose one authentication mode, then export it:
set -a; source .env; set +a

Review the config file:

cat configs/searchqa/default.yaml

Key parameters (deep learning analogy in parentheses):

train:
  num_epochs: 4           # (epochs)
  batch_size: 40          # (batch size)

optimizer:
  learning_rate: 4        # (max edits per step)
  lr_scheduler: cosine    # (learning rate schedule)
  use_slow_update: true   # (momentum at epoch boundary)
  use_meta_skill: true    # (cross-epoch optimizer memory)

gradient:
  analyst_workers: 16     # (parallel reflection workers)

evaluation:
  use_gate: true          # (validation gating)

4. Train

python scripts/train.py \
  --config configs/searchqa/default.yaml \
  --out_root outputs/searchqa_first_run

The command prints the resolved backend/data configuration, per-step rollout and gate progress, and the generated output directory.

5. Monitor

The explicit --out_root above creates this run directory:

outputs/searchqa_first_run/
├── config.json
├── runtime_state.json
├── history.json
├── best_skill.md
├── skills/
│   └── skill_vXXXX.md
├── steps/
│   └── step_XXXX/
│       ├── candidate_skill.md
│       ├── step_record.json
│       └── trajectory_digest.json
├── slow_update/
│   └── epoch_XX/
└── meta_skill/
    └── epoch_XX/

6. Evaluate

Evaluate the best skill on the test split:

python scripts/eval_only.py \
  --config configs/searchqa/default.yaml \
  --skill outputs/searchqa_first_run/best_skill.md \
  --split valid_unseen

The --skill path above is the training artifact. Evaluation writes eval_summary.json to its own timestamped outputs/eval_.../ directory unless you pass an explicit --out_root; it does not overwrite the training run.

WebUI

Prefer a graphical interface? Launch the WebUI:

pip install -e ".[webui]"
python -m skillopt_webui.app

Then open http://localhost:7860 in your browser to configure parameters and launch training. The default host is 0.0.0.0; pass --host 127.0.0.1 for a local-only dashboard.

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