execution-plan.md

SPIRAL Demo App Execution Plan

This execution plan outlines the development of a practical, interactive tool on Hugging Face Spaces based on the SPIRAL paper ("Self-Play on Zero-Sum Games Incentivizes Reasoning via Multi-Agent Multi-Turn Reinforcement Learning"). The tool will be an Interactive Reasoning Game Simulator: Users can play zero-sum games (e.g., Kuhn Poker, TicTacToe) against a self-play trained AI, view step-by-step reasoning traces, and test the AI's transferred reasoning skills on non-game tasks like math problems or logic puzzles.

Utility Focus:

• Non-Technical Users: Simple web interface to play games, learn about AI reasoning through visualizations, and experiment with prompts for educational fun (e.g., "How does AI think in games?").
• Technical Users: Access to model weights, training scripts, and APIs for extending the self-play system (e.g., custom games or fine-tuning).
• Practicality: Free to use, no setup required; demonstrates real-world AI applications in strategy, education, and decision-making. Aims for broad appeal: 1000+ users via HF community sharing.

The plan is divided into phases with checkboxes for sub-tasks. Each phase includes detailed "how" steps.

Phase 1: Research and Planning

• Review SPIRAL Paper and Gather Resources
  • How: Read the full paper (use attached snips as reference). Identify key components: self-play RL on games like Kuhn Poker, role-conditioned advantage estimation (RAE), multi-agent multi-turn training. Download base models (e.g., Qwen-4B from HF) and RL libs (Gym, Stable Baselines). Collect datasets: Simple game rules/implementations from GitHub; math benchmarks like GSM8K for transfer testing.
• Define Tool Features
  • How: Brainstorm user flows. Core: Game mode (user vs. AI play), Reasoning Viewer (display traces), Transfer Tester (input math/logic queries). Add tutorials for non-tech users, exportable logs for tech users. Ensure accessibility: Mobile-friendly UI, low-latency inference.
• Scope Requirements and Tech Stack
  • How: Choose Python for backend; Gradio for HF Spaces UI (easy interactive elements like buttons for moves). Use Transformers for LLM, Gym for games, PPO from Stable Baselines for RL demo. Estimate: 1-2 weeks dev time, free HF tier for hosting (upgrade to GPU if needed for training demos).

Phase 2: Implementation

• Set Up Project Structure
  • How: Create a Git repo. Folders: src/ for code, models/ for weights, data/ for game datasets, app/ for Gradio script. Initialize with requirements.txt: transformers, torch, gymnasium, stable-baselines3, gradio.
• Implement Game Environments
  • How: Code Gym envs for Kuhn Poker/TicTacToe (e.g., class KuhnPokerEnv(gym.Env) with action_space, observation_space, reward for wins). Add multi-turn logic: Track game state, player turns.
• Train SPIRAL Model
  • How: Load base LLM (Qwen-4B). Implement self-play: Clone agent, train via PPO with RAE (custom advantage function: advantage = reward + value - baseline, conditioned on roles like 'player' vs. 'opponent'). Train on 1000+ episodes (simulate self-improvement). Save checkpoints to HF Model Hub.
• Build Reasoning and Transfer Components
  • How: For games, generate traces (e.g., "Opponent bet high → Likely strong hand → Fold"). For transfer, prompt model with math tasks post-training. Use chain-of-thought prompting for visibility.
• Develop User Interface
  • How: Use Gradio Blocks: Tab 1: Game Play (dropdown for game, text input for moves, output panel for AI response/trace). Tab 2: Tester (input prompt, show output). Add buttons for "Explain Reasoning" and "Export Session". Style with CSS for modern UX (e.g., cards, animations).

Phase 3: Testing and Optimization

• Unit and Integration Testing
  • How: Test game logic (e.g., assert win conditions). Run self-play simulations to verify improvements (e.g., win rate >50% after training). Use pytest for automation.
• User Testing
  • How: Simulate non-tech users (play games, check intuitiveness). For tech users, test API endpoints. Gather feedback via HF Spaces comments or a built-in form. Measure metrics: Latency <2s per move, accuracy on benchmarks (+8% as per paper).
• Optimize for HF Spaces
  • How: Profile for CPU/GPU usage; use model quantization (e.g., bitsandbytes) for faster inference. Ensure no interactive flags needed (e.g., --yes for installs).

Phase 4: Deployment and Documentation

• Deploy to Hugging Face Spaces
  • How: Create Space, upload repo via Git. Set entry point to Gradio app.py. Enable public access, add tags like "AI", "Games", "Reasoning" for discoverability.
• Create Documentation and Tutorials
  • How: Write README.md with paper summary, usage guide (screenshots), and code explanations. Add in-app help: Tooltips for buttons, video demo. For tech users: Include training scripts and extension guides.
• Launch and Promote
  • How: Share on HF forums, Reddit (r/MachineLearning), Twitter. Monitor usage via HF analytics; iterate based on feedback (e.g., add more games).

Phase 5: Maintenance and Iteration

• Monitor and Update
  • How: Check for issues (e.g., via GitHub Issues). Update model with new games or better RL algos. Aim for v2: Multimodal (add image-based games).
• Measure Impact
  • How: Track metrics: User sessions, feedback ratings. Goal: 1000+ interactions in first month, positive reviews highlighting educational value.

This plan ensures a useful tool that's easy to use, educational, and extensible.