Skill-Pro

This is the official implementation of the paper Skill-Pro: Learning Reusable Skills from Experience via Non-Parametric PPO for LLM Agents (Qirui Mi, Zhijian Ma, Mengyue Yang, Haoxuan Li, Yisen Wang, Haifeng Zhang, Jun Wang), accepted as a Spotlight at ICML 2026.

Skill-Pro is a Skill-augmented Markov Decision Process (Skill-MDP, SMDP) framework for LLM Agents. On top of a vanilla LLM-Agent decision loop, it introduces an evolvable Skill Pool, where each skill is described by a three-part Initiation / Policy / Termination schema, similar to the classical Options framework. At every step, the agent first selects an appropriate skill and injects it into the prompt, then lets the LLM produce an action. In parallel, a Skill Evolution module continuously generates, refines, and prunes skills based on interaction experience via Non-Parametric PPO (semantic-gradient candidate generation + a PPO Gate for verification), enabling online evolution of the agent's capabilities without any parameter updates.

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Features

• Skill-MDP / vanilla MDP modes: switch via --MDP_type {SMDP, MDP} to enable or disable skills.
• Evolvable Skill Pool: ships with a set of general reasoning skills (StructuredCoT, ReActDecision, HypothesisElimination, etc.), maintained online during training.
• Multiple skill-selection strategies: supports llm_model (LLM picks the skill) and llm_topk_lcb (Top-K retrieval + LCB), among others.
• Multiple environments: built on TextArena and ALFWorld; already adapted to Mastermind-v0, FrozenLake, Hangman, alfworld, and more.
• Local and remote LLM backends: local models are served via vLLM; remote models are accessed through OpenRouter.
• Rich experiment logging: training curves are tracked with SwanLab, and per-iteration skill snapshots, rewards, and maintenance logs are written to JSON.
• Ablation switches: --ablation_type supports wo_sg / wo_ppo / wo_score / none for paper-grade reproducibility.

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Project Structure

skill_Pro/
├── main.py                  # Entry point: parses CLI args and launches SkillMDP
├── run.py                   # Core training / evaluation loop (SkillMDP)
├── data_structures.py       # Skill / Experience dataclasses
├── pool_managers.py         # ExperiencePool / GoldenExperiencePool
├── Skills/
│   ├── skill_pool.py        # Skill Pool (selection, retrieval, maintenance)
│   ├── skill_evolution.py   # Skill evolution and verification
│   └── loss.py              # Log-prob and other training losses
├── utils/
│   ├── local_llm.py         # Local LLM wrapper (vLLM / HF)
│   ├── encode.py            # Text embedding (sentence-transformers)
│   ├── models.py            # Model-side utilities
│   └── utils.py             # Logging paths, random seeds, etc.
├── configs/
│   └── base_config.yaml     # Configuration for ALFWorld and other envs
├── requirements.txt
└── README.md

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Installation (Windows + Conda)

We recommend Python 3.10. Run the following in Anaconda Prompt or PowerShell:

git clone https://github.com/<your-org>/Skill_Pro.git
cd Skill_Pro

conda create -n Skillmdp python=3.10 -y
conda activate Skillmdp

pip install -r requirements.txt

Tip: if conda activate fails the first time you use it in PowerShell, run conda init powershell once and restart the terminal.

ALFWorld Data Preparation (optional)

To run ALFWorld tasks, download the data and set the data path (PowerShell syntax):

pip install alfworld
$env:ALFWORLD_DATA = "D:\path\to\alfworld_data"
alfworld-download

The $ALFWORLD_DATA placeholder in configs/base_config.yaml will be expanded automatically.

LLM Backends

• Local model: pass a local path to --agent_name, e.g. /mnt/.../gemma-2-9b-it. The model is loaded via vLLM internally.
• Remote model: any OpenRouter-served model, e.g. --agent_name meta-llama/llama-3.3-70b-instruct. Set the OPENROUTER_API_KEY environment variable beforehand.

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Quick Start

Training (with Skill Evolution)

python main.py `
  --MDP_type SMDP `
  --env_names Mastermind-v0 `
  --agent_name meta-llama/llama-3.3-70b-instruct `
  --ge_model_name meta-llama/llama-3.3-70b-instruct `
  --max_iters 50 `
  --episodes_per_iter 5 `
  --pool_size 10 `
  --select_type llm_model

Each outer iteration will:

1. Run episodes_per_iter episodes in every environment.
2. Update each skill's frequency / avg_gain / maturity statistics from the collected trajectories.
3. Invoke SkillEvolution for skill evolution and verification.
4. Log to SwanLab and write outputs/*_training_logs.json.

Evaluation Only (load an existing Skill Pool)

python main.py `
  --MDP_type SMDP `
  --test `
  --env_names Mastermind-v0 `
  --agent_name meta-llama/llama-3.3-70b-instruct `
  --load_pool_path ./outputs/xxx_training_logs.json `
  --load_iteration 1

Ablation Studies

# Disable Skill Generation
python main.py --ablation_type wo_sg ...

# Disable score-based maintenance, use FIFO instead
python main.py --ablation_type wo_score ...

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Key Arguments

Argument	Default	Description
--MDP_type	SMDP	SMDP enables skills; MDP is pure LLM decision-making
--ablation_type	none	One of wo_sg / wo_ppo / wo_score / none
--env_names	Mastermind-v0	Comma-separated list of task names
--agent_name	gemma/llama, etc.	Decision LLM (local path or remote ID)
--ge_model_name	llama-3.3-70b	LLM used for Skill Evolution
--select_type	llm_model	Skill selection strategy
--pool_size	10	Maximum size of the Skill Pool
--max_iters	50	Number of outer iterations
--episodes_per_iter	5	Episodes per environment per iteration
--epsilon_initial	0.3	Initial ε for skill selection
--skill_select_k	1	Number of skill-selection steps
--topk	5	Top-K used during retrieval
--output_dir	outputs	Directory for training logs

See parse_arguments in main.py for the full list.

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Outputs and Logging

• JSON training logs: outputs/<env>_<MDP>_<select>_<agent>_<ge_model>_<timestamp>_training_logs.json, containing:
  • Per-iteration avg_return, task_baselines, and global_baseline
  • pool_snapshot: snapshot of the Skill Pool (including initiation / policy / termination / freq / gain / maturity)
  • evolution_details and maintenance_details: evolution and maintenance traces
  • delta_prompt_tokens_per_step: extra prompt tokens introduced by skill injection
• SwanLab: the experiment name is auto-composed from CLI arguments and tracks per-environment episode return, baseline, ε, and more.

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Design Overview

            ┌─────────────────────┐
            │     SkillPool       │
            │  (Initiation/Policy │
            │   /Termination)     │
            └──────────┬──────────┘
                       │ select_skill(state)
                       ▼
state ─►  build_decision_prompt() ─►  LLM Policy ─► action ─► Env
                       ▲                                       │
                       │ trajectory + reward                   │
                       │                                       ▼
                ExperiencePool / GoldenExperiencePool ◄────────┘
                       │
                       ▼
                 SkillEvolution
        (generate / refine / verify / score / prune skills)

• Skill selection: SkillPool.select_skill decides whether to activate a skill given the current state, ε, and select_type.
• Skill termination: SkillMDP._should_terminate uses a meta-controller LLM to decide whether the active skill should stop.
• Skill maintenance: maintain / maintain_fifo prune the pool by freq × avg_gain score or by FIFO.
• Skill evolution: SkillEvolution.run_skill_evolution_with_verification combines recent experiences with the Golden Experience Pool to generate and verify new skills.

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FAQ

• vLLM fails to load? Check tensor_parallel_size (default is 4); set it to 1 on a single-GPU machine and make sure VRAM is sufficient.
• OpenRouter returns 401? Set the environment variable: in PowerShell, $env:OPENROUTER_API_KEY = "...".
• Running non-ALFWorld tasks without ALFWorld installed? The code falls back gracefully and skips ALFWorld initialization automatically.

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Citation

If you find this work useful, please consider citing our ICML Spotlight paper:

@inproceedings{mi2026skillpro,
  title     = {Skill-Pro: Learning Reusable Skills from Experience via Non-Parametric PPO for LLM Agents},
  author    = {Mi, Qirui and Ma, Zhijian and Yang, Mengyue and Li, Haoxuan and Wang, Yisen and Zhang, Haifeng and Wang, Jun},
  booktitle = {Proceedings of the 43rd International Conference on Machine Learning (ICML 2026)},
  year      = {2026},
  note      = {Spotlight},
  url       = {https://arxiv.org/abs/2602.01869}
}

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License

This project is released under the MIT License. See the LICENSE file at the repository root for details.