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.

🎬 SkillOpt Demo Video

64c8f76086bed7bd7a5ce664a7a14f40_raw.mp4

▶ Watch the full demo on YouTube

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Install

Requirements: Python 3.10+

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

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

Azure OpenAI (recommended):

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 always required. Without it, all LLM calls will fail.

OpenAI directly:

export OPENAI_API_KEY="sk-..."

Anthropic Claude:

export ANTHROPIC_API_KEY="sk-ant-..."

Qwen (local vLLM):

export QWEN_CHAT_BASE_URL="http://localhost:8000/v1"
export QWEN_CHAT_MODEL="Qwen/Qwen3.5-4B"

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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:

[
  {
    "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

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Quick Start

Training

# 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:

# Evaluate on test set only:
python scripts/eval_only.py \
  --config configs/searchqa/default.yaml \
  --skill outputs/my_run/best_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 outputs/my_run/best_skill.md \
  --split all \
  --split_dir /path/to/searchqa_split \
  --azure_openai_endpoint https://your-resource.openai.azure.com/

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.

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WebUI

Launch the monitoring dashboard (optional):

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

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

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Citation

@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}
}