# Improved NPT Implementation Summary

## Overview
The improved NPT (Nuisance-Prompt Tuning) implementation adds gradient clipping with adaptive weight decay to enhance training stability and improve OOD detection performance.

## Key Improvements

### 1. Gradient Clipping
- **Implementation**: Uses `torch.nn.utils.clip_grad_norm_()` to constrain gradient magnitudes
- **Default**: `max_grad_norm = 1.0`
- **Benefit**: Prevents exploding gradients that can destabilize prompt learning
- **Location**: Applied after `loss.backward()` in `train_epoch()` method

### 2. Adaptive Weight Decay
- **Formula**: `adaptive_wd = base_wd * (1 + grad_norm_scale_factor * current_grad_norm)`
- **Default Parameters**:
  - `base_weight_decay = 1e-4`
  - `grad_norm_scale_factor = 0.1`
- **Benefit**: Stronger regularization when gradients are large, lighter when gradients are small
- **Location**: Updated dynamically in optimizer parameter groups during training

### 3. Enhanced Monitoring
- **Gradient Norms**: Tracked per batch and averaged per epoch
- **Adaptive Weight Decay**: Logged for each batch and epoch
- **History Tracking**: Maintains `grad_norm_history` and `adaptive_wd_history`
- **Output**: Enhanced training logs show `grad_norm` and `adapt_wd` values

## Technical Details

### New Parameters
```python
--max-grad-norm (float): Maximum gradient norm for clipping (default: 1.0)
--base-weight-decay (float): Base weight decay value (default: 1e-4)  
--grad-norm-scale-factor (float): Scaling factor for adaptive weight decay (default: 0.1)
```

### Training Loop Changes
1. After `loss.backward()`:
   - Apply gradient clipping → get gradient norm
   - Calculate adaptive weight decay based on gradient norm
   - Update optimizer weight decay
   - Proceed with `optimizer.step()`

### Interface Compatibility
- **Input**: Same as original NPT (--output-dir required, additional parameters optional)
- **Output**: Identical file formats (`scores.npz`, `results.json`)
- **Evaluation**: Same OOD detection pipeline and metrics

## Expected Benefits
1. **Training Stability**: Gradient clipping prevents training instabilities
2. **Better Generalization**: Dynamic regularization adapts to training dynamics
3. **Improved OOD Performance**: More stable prompt learning should improve feature separation
4. **Robust Convergence**: Adaptive approach handles varying gradient scales automatically

## Usage Example
```bash
# Basic usage (uses default hyperparameters)
python improved_proposed_method.py --output-dir ./results

# Custom hyperparameters
python improved_proposed_method.py --output-dir ./results \
  --max-grad-norm 0.5 \
  --base-weight-decay 5e-5 \
  --grad-norm-scale-factor 0.05
```

## File Structure
- `improved_proposed_method.py`: Main implementation
- `plan_improved_proposed_method.md`: Implementation plan
- `validate_improved.py`: Validation tests for core functionality
- `IMPROVEMENTS_SUMMARY.md`: This summary document