# SkillOpt: Executive Strategy for Self-Evolving Agent Skills

*Train agent skills like you train neural networks — with epochs, (mini-)batchsize, learning rates, and validation gates — but without touching model weights.*

[![Project Page](https://img.shields.io/badge/Project%20Page-SkillOpt-8dbb3c)](https://microsoft.github.io/SkillOpt/) [![Paper](https://img.shields.io/badge/Paper-arXiv-b31b1b)](https://arxiv.org/abs/2605.23904) [![Project Video](https://img.shields.io/badge/Project%20Video-Watch%20Demo-ff0000)](https://youtu.be/JUBMDTCiM0M) [![Python 3.10+](https://img.shields.io/badge/Python-3.10%2B-blue.svg)](https://www.python.org/) [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](LICENSE)

## 🎬 SkillOpt Demo Video

https://github.com/user-attachments/assets/eb12d3bc-371c-467f-904d-91b61f339ed7

<p align="center">
  <a href="https://youtu.be/JUBMDTCiM0M"><b>▶ Watch the full demo on YouTube</b></a>
</p>

---

## Install

**Requirements:** Python 3.10+

```bash
git clone https://github.com/microsoft/SkillOpt.git
cd SkillOpt
pip install -e .

# For ALFWorld benchmark (optional):
pip install -e ".[alfworld]"
alfworld-download
```

### Configure API Credentials

```bash
cp .env.example .env
# Edit .env with your API credentials, then:
source .env
```

**Azure OpenAI** (recommended):
```bash
export AZURE_OPENAI_ENDPOINT="https://your-resource.openai.azure.com/"
# Option 1: API key auth
export AZURE_OPENAI_API_KEY="your-key"
# Option 2: Azure CLI auth (no API key needed)
export AZURE_OPENAI_AUTH_MODE="azure_cli"
```

> **Note:** `AZURE_OPENAI_ENDPOINT` is required for all three modes (`api_key`, `azure_cli`,
> `openai_compatible`). Without it, all LLM calls will fail.

**OpenAI-compatible endpoints**:
```bash
export AZURE_OPENAI_ENDPOINT="https://api.openai.com/v1"
export AZURE_OPENAI_API_KEY="sk-..."
export AZURE_OPENAI_AUTH_MODE="openai_compatible"
```

This routes all calls through the plain OpenAI Python client (no Azure auth, no `api-version`
header).

> **Note:** SkillOpt reuses the `AZURE_OPENAI_*` env var names even in this mode — there is no
> separate `OPENAI_API_KEY` knob.

**Anthropic Claude**:
```bash
export ANTHROPIC_API_KEY="sk-ant-..."
```

**Qwen (local vLLM)**:
```bash
export QWEN_CHAT_BASE_URL="http://localhost:8000/v1"
export QWEN_CHAT_MODEL="Qwen/Qwen3.5-4B"
```

---

## Data Preparation

SkillOpt expects data in a **split directory** with `train/`, `val/`, `test/` subdirectories, each containing a JSON file (e.g., `items.json`).

```
data/my_split/
├── train/items.json
├── val/items.json
└── test/items.json
```

Each JSON file is an array of task items. The required fields depend on the benchmark. For example, SearchQA items look like:

```json
[
  {
    "id": "unique_item_id",
    "question": "Who wrote the novel ...",
    "context": "[DOC] relevant passage text ...",
    "answers": ["expected answer"]
  }
]
```

See `skillopt/envs/<benchmark>/dataloader.py` for the exact format each benchmark expects.

> **Note:** Benchmark datasets are not included in this repository. Prepare your own data following the format above.

### Supported Benchmarks

| Benchmark | Type | Config |
|---|---|---|
| SearchQA | QA | `configs/searchqa/default.yaml` |
| ALFWorld | Embodied agent | `configs/alfworld/default.yaml` |
| DocVQA | Document QA | `configs/docvqa/default.yaml` |
| LiveMathematicianBench | Math | `configs/livemathematicianbench/default.yaml` |
| SpreadsheetBench | Code generation | `configs/spreadsheetbench/default.yaml` |
| OfficeQA | Tool-augmented QA | `configs/officeqa/default.yaml` |

---

## Quick Start

### Training

```bash
# Minimal example — train on SearchQA:
python scripts/train.py \
    --config configs/searchqa/default.yaml \
    --split_dir /path/to/your/searchqa_split \
    --azure_openai_endpoint https://your-resource.openai.azure.com/ \
    --optimizer_model gpt-5.5 \
    --target_model gpt-5.5

# Train on LiveMathematicianBench:
python scripts/train.py \
    --config configs/livemathematicianbench/default.yaml \
    --split_dir /path/to/your/livemath_split \
    --azure_openai_endpoint https://your-resource.openai.azure.com/ \
    --optimizer_model gpt-5.5 \
    --target_model gpt-5.5

# Train on ALFWorld:
python scripts/train.py \
    --config configs/alfworld/default.yaml \
    --split_dir /path/to/your/alfworld_split \
    --azure_openai_endpoint https://your-resource.openai.azure.com/ \
    --optimizer_model gpt-5.5 \
    --target_model gpt-5.5
```

Key CLI arguments:

| Argument | Description | Example |
|---|---|---|
| `--config` | Benchmark config YAML | `configs/searchqa/default.yaml` |
| `--split_dir` | Path to data split directory | `/path/to/split` |
| `--azure_openai_endpoint` | Azure OpenAI endpoint URL | `https://your-resource.openai.azure.com/` |
| `--optimizer_model` | Optimizer model deployment name | `gpt-5.5` |
| `--target_model` | Target model deployment name | `gpt-5.5` |
| `--num_epochs` | Number of training epochs | `4` |
| `--batch_size` | Batch size per step | `40` |
| `--workers` | Parallel rollout workers | `8` |
| `--out_root` | Output directory | `outputs/my_run` |

### Eval Only

Evaluate a trained skill on specific data splits without training:

```bash
# Evaluate the packaged GPT-5.5 SearchQA skill on the test split:
python scripts/eval_only.py \
  --config configs/searchqa/default.yaml \
  --skill ckpt/searchqa/gpt5.5_skill.md \
  --split valid_unseen \
  --split_dir /path/to/searchqa_split \
  --azure_openai_endpoint https://your-resource.openai.azure.com/

# Evaluate on all splits (train + val + test):
python scripts/eval_only.py \
  --config configs/searchqa/default.yaml \
  --skill ckpt/searchqa/gpt5.5_skill.md \
  --split all \
  --split_dir /path/to/searchqa_split \
  --azure_openai_endpoint https://your-resource.openai.azure.com/
```

To evaluate a skill produced by a training run, replace `--skill` with that
run's best-skill path, for example `outputs/my_run/best_skill.md`.

| Split | Description |
|---|---|
| `valid_unseen` | Test set |
| `valid_seen` | Validation set |
| `train` | Training set |
| `all` | All splits combined (default) |

### Output Structure

Each run writes to a structured output directory:

```
outputs/<run_name>/
├── config.json              # Flattened runtime config
├── history.json             # Per-step training history
├── runtime_state.json       # Resume checkpoint
├── best_skill.md            # Best validated skill document
├── skills/skill_vXXXX.md   # Skill snapshot per step
├── steps/step_XXXX/        # Per-step artifacts (patches, evals)
├── slow_update/epoch_XX/   # Slow update logs
└── meta_skill/epoch_XX/    # Meta skill logs
```

Re-running the same command auto-resumes from the last completed step.

---

## Community-contributed configs

These are **not** default SkillOpt settings — they are reference configs
contributed by users for specific scenarios. The paper-reported numbers
were obtained with the default settings, not these.

- **`configs/examples/soft_gate.yaml`** *(PR #25, contributed by
  [@lvbaocheng](https://github.com/lvbaocheng))* — switches the
  validation gate from exact-match (`hard`) to soft / partial-credit
  (`soft` or `mixed`). Useful when the held-out **selection split is
  small** (e.g. ≤ ~10 items) and the **reward is continuous**, where the
  discrete hard gate often rejects every candidate and training stalls.
  See the comment at the top of the file for details and when not to use
  it.

---

## WebUI

Launch the monitoring dashboard (optional):

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

| Flag | Default | Description |
|---|---|---|
| `--port` | 7860 | Server port |
| `--host` | `0.0.0.0` | Bind address |
| `--share` | off | Create a public Gradio share link |

```bash
# With public share link (useful for remote servers)
python -m skillopt_webui.app --share
```

---

## Citation

```bibtex
@misc{yang2026skilloptexecutivestrategyselfevolving,
      title={SkillOpt: Executive Strategy for Self-Evolving Agent Skills}, 
      author={Yifan Yang and Ziyang Gong and Weiquan Huang and Qihao Yang and Ziwei Zhou and Zisu Huang and Yan Li and Xuemei Gao and Qi Dai and Bei Liu and Kai Qiu and Yuqing Yang and Dongdong Chen and Xue Yang and Chong Luo},
      year={2026},
      eprint={2605.23904},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://arxiv.org/abs/2605.23904}
}
```
