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:
```python
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

```bash
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?