teacher_agent_dev/UPDATE_SUMMARY.md

Update Summary: Using LM Student in Comparison

✅ Changes Completed

Updated compare_strategies.py to use LM Student (DistilBERT) instead of MockStudentAgent for all three strategies:

1. Random Strategy - Now uses LM Student
2. Progressive Strategy - Now uses LM Student
3. Teacher Strategy - Now uses LM Student

🔧 Technical Changes

1. Added LM Student Import

• Added path to student_agent_dev directory
• Imports StudentAgent from student_agent.py as LMStudentAgent
• Falls back to MockStudentAgent if import fails

2. Updated All Three Strategy Functions

• train_strategy_random() - Uses LM Student
• train_strategy_progressive() - Uses LM Student
• train_strategy_teacher() - Uses LM Student

3. LM Student Configuration

All strategies use:

student = LMStudentAgent(
    learning_rate=5e-5,           # LM fine-tuning learning rate
    retention_constant=80.0,      # Slower forgetting
    device='cpu',                 # CPU for compatibility
    max_length=256,               # Max tokens
    gradient_accumulation_steps=4 # Stability
)

4. Fallback Support

If LM Student cannot be imported, automatically falls back to MockStudentAgent.

📝 How to Run

cd teacher_agent_dev

# Quick test (50 iterations)
python compare_strategies.py --iterations 50 --deterministic

# Full comparison (500 iterations - will take longer with LM)
python compare_strategies.py --iterations 500 --deterministic

⚠️ Performance Notes

LM Student is much slower than MockStudentAgent because:

• Each answer() call runs DistilBERT inference
• Each learn() call fine-tunes DistilBERT (forward + backward pass)
• Memory decay calculations

Expected runtime:

• MockStudentAgent: ~30 seconds for 500 iterations
• LM Student: ~15-30 minutes for 500 iterations

🔍 What to Expect

With LM Student:

• More realistic learning: Actual neural network learning vs simple skill tracking
• Slower convergence: LM needs more examples to learn patterns
• Different results: LM behavior differs from mock student
• Memory decay: Ebbinghaus forgetting curve affects LM predictions

✅ Verification

The code is ready to run. When you execute:

1. You'll see: ✅ Using LM Student (DistilBERT) if import succeeds
2. Or: ⚠️ Could not import LM Student if transformers library missing
3. All three strategies will use the same student type

🚀 Next Steps

Run the comparison and analyze results:

• Do teacher strategy still outperform random/progressive?
• How does LM learning differ from mock student?
• What patterns emerge with real neural network learning?