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# Student Language Model Agent
DistilBERT-based student agent with online learning and memory decay for AI teacher-student system.
## Quick Start
1. Install dependencies:
```bash
pip install -r requirements.txt
```
2. Run tests:
```bash
python test_student.py
```
3. Train student:
```bash
python train_student.py
```
4. Check visualizations:
```bash
ls student_visualizations/
```
## Features
- **Online Learning**: Fine-tunes on 1 task at a time (not batches)
- **Memory Decay**: Realistic forgetting using Ebbinghaus curves
- **Per-Topic Tracking**: Monitors progress separately for each topic
- **Comprehensive Metrics**: Learning rate, sample efficiency, retention analysis
- **Beautiful Visualizations**: 6+ publication-quality plots
## Integration with Other Components
### With Real Teacher Agent:
Replace `MockTeacherAgent` with real `TeacherAgent` in `train_student.py`
### With Real Task Generator:
Replace `MockTaskGenerator` with real `TaskGenerator` in `train_student.py`
### Interface Compatibility:
All components follow the interfaces in `interfaces.py` - as long as the interface is respected, components are plug-and-play.
## Key Parameters
- `learning_rate`: How fast student learns (default: 5e-5)
- `retention_constant`: Forgetting speed (default: 80.0, higher = slower forgetting)
- `max_length`: Max tokens for passage+question (default: 256)
- `gradient_accumulation_steps`: Stability for online learning (default: 4)
## Metrics Generated
- Overall accuracy curve
- Per-topic learning curves
- Retention/forgetting analysis
- Difficulty progression
- Topic distribution
- Sample efficiency (tasks to reach milestones)
## File Structure
- `student_agent.py` - Main DistilBERT student
- `memory_decay.py` - Ebbinghaus forgetting model
- `student_metrics.py` - Metrics tracking
- `visualize_student.py` - Plotting utilities
- `train_student.py` - Training script
- `test_student.py` - Unit tests
- `mock_teacher.py` - Dummy teacher for testing
- `mock_task_generator.py` - Dummy task generator for testing
## Expected Behavior
Student should:
1. Start at ~25% accuracy (random guessing on 4-choice MCQ)
2. Improve to 70-80% with practice
3. Forget over time when topics not reviewed
4. Learn faster on easy tasks, slower on hard tasks
5. Show per-topic specialization
## Troubleshooting
**Student not improving:**
- Increase `learning_rate` (try 1e-4)
- Train for more iterations
- Check task quality
**Forgetting too fast/slow:**
- Adjust `retention_constant`
- Higher value = slower forgetting
**Out of memory:**
- Use `device='cpu'`
- Reduce `max_length`
- Increase `gradient_accumulation_steps`
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