student_agent_dev/README.md

Student Language Model Agent

DistilBERT-based student agent with online learning and memory decay for AI teacher-student system.

Quick Start

1. Install dependencies:

pip install -r requirements.txt

2. Run tests:

python test_student.py

3. Train student:

python train_student.py

4. Check visualizations:

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