README_HF_SPACE.md

metadata

title: MentorFlow
emoji: 🎓
colorFrom: blue
colorTo: purple
sdk: gradio
sdk_version: 5.9.0
app_file: app.py
pinned: false
license: mit
hardware: gpu-t4

MentorFlow - Teacher-Student RL System

A meta-curriculum reinforcement learning system where an AI Teacher Agent learns to select optimal educational tasks to train an AI Student Agent.

🚀 Features

• Three Training Strategies: Compare Random, Progressive, and Teacher-guided curriculum
• LM Student (DistilBERT): Real neural network learning with memory decay
• GPU Support: Fast training with CUDA acceleration
• Interactive Comparison: Visualize learning curves and performance metrics

📊 Usage

1. Set Parameters:

   • Iterations: Number of training iterations (50-500)
   • Seed: Random seed for reproducibility
   • Device: Choose GPU (cuda) or CPU

2. Run Comparison:

   • Click "Run Comparison" to start training
   • Monitor progress in the output text
   • View generated comparison plots

3. Analyze Results:

   • Learning curves show how each strategy improves
   • Difficult question performance shows final accuracy
   • Curriculum diversity shows topic coverage

⚡ Performance

• With GPU: ~5-10 minutes for 500 iterations
• With CPU: ~15-30 minutes for 500 iterations

📁 Project Structure

MentorFlow/
├── app.py                      # Gradio web interface
├── teacher_agent_dev/          # Teacher agent system
│   ├── compare_strategies.py  # Main comparison script
│   ├── teacher_agent.py       # UCB bandit teacher
│   └── ...
├── student_agent_dev/          # LM Student system
│   ├── student_agent.py       # DistilBERT student
│   └── ...
└── requirements_hf.txt        # Dependencies

🔧 Technical Details

• Teacher Agent: UCB (Upper Confidence Bound) multi-armed bandit
• Student Agent: DistilBERT with online learning
• Memory Decay: Ebbinghaus forgetting curve
• Task Generator: Procedural generation with 15 topics × 7 difficulties

📖 More Information

See the main repository for detailed documentation and development guides.