Mode Collapse Prevention: Comprehensive Training Improvements

Status: 🟡 In Progress - Training Deployed
Type: Implementation + Training Experiment

Objective

Address the catastrophic mode collapse discovered in the 1000-epoch training run, where the model generated only punctuation marks instead of coherent Shakespeare-style text. Implement comprehensive training improvements to prevent token frequency exploitation and ensure stable, quality text generation.

This experiment builds directly on the failure analysis from the Learned Rounding Implementation post-hoc analysis, where extended training led to severe quality degradation.

Background

The Mode Collapse Problem

The 1000-epoch training run (Issue #17) resulted in devastating quality regression:

Sample Output:

:::,:,,:,,,::,:,::::,::,,:,
,,,,,,,,,:::,:,:,::, him:,,,'::,,,,: 
,:

Instead of learning nuanced Shakespeare language patterns, the model exploited high-frequency punctuation tokens in the training data distribution, leading to complete semantic collapse.

Root Cause Analysis

Primary Failure Modes Identified: 1. Token Frequency Bias: Punctuation tokens (,, :) dominate corpus statistics 2. Learning Rate Instability: Fixed LR over 1000 epochs destabilized learned embeddings 3. Dual-Loss Imbalance: Rounding loss overwhelmed diffusion objective over extended training
4. Lack of Regularization: No dropout, weight decay, or validation monitoring 5. No Early Stopping: Training continued far past optimal generalization point

Code Implementation

Pull Request

All training improvements implemented in PR #19: Fix Mode Collapse: Comprehensive Training Improvements - comprehensive 200+ line overhaul of training infrastructure.

Key Components Added

1. Learning Rate Scheduling

def get_cosine_schedule_with_warmup(optimizer, num_warmup_steps, num_training_steps, eta_min=0):
    """Cosine annealing learning rate schedule with warmup."""
    def lr_lambda(current_step):
        if current_step < num_warmup_steps:
            return float(current_step) / float(max(1, num_warmup_steps))
        progress = float(current_step - num_warmup_steps) / float(max(1, num_training_steps - num_warmup_steps))
        return max(eta_min, 0.5 * (1.0 + math.cos(math.pi * progress)))

    return torch.optim.lr_scheduler.LambdaLR(optimizer, lr_lambda)

Features: - Smooth learning rate transitions prevent training instability - Warmup phase allows gradual parameter optimization - Cosine annealing provides natural training termination

2. Regularization Framework

class TinyTransformer(nn.Module):
    def __init__(self, dim, n_heads=4, depth=3, dropout=0.1):
        # Dropout-enabled transformer layers
        encoder_layer = nn.TransformerEncoderLayer(
            d_model=dim, nhead=n_heads, batch_first=True, dropout=dropout
        )
        self.dropout = nn.Dropout(dropout)

Features: - Configurable dropout rates (0.1-0.2) prevent overfitting - Weight decay (1e-4) for L2 regularization - Applied to transformer layers and embeddings

3. Dynamic Loss Rebalancing

def dynamic_rounding_weight_schedule(epoch, total_epochs, initial_weight=1.0, final_weight=0.1):
    """Decay rounding weight over training to prevent overfitting to token prediction."""
    progress = epoch / total_epochs
    return initial_weight * (1 - progress) + final_weight * progress

Features: - Starts with strong token prediction signal (0.5) - Gradually reduces to 10% of initial value over training - Prevents rounding loss from dominating diffusion objective

4. Validation & Early Stopping

def tokenize_corpus(text: str, tokenizer, seq_len: int, val_split=0.1):
    """Tokenize corpus with automatic train/validation split."""
    # Split into train/val
    n_val = int(n_chunks * val_split)
    n_train = n_chunks - n_val
    train_chunks, val_chunks = random_split(chunks, [n_train, n_val])
    return train_chunks, val_chunks

Features: - Automatic 90/10 train/validation split - Early stopping with configurable patience (5-10 epochs) - Best model checkpoint saving based on validation performance - Comprehensive train/val loss monitoring

Enhanced CLI Arguments

New Training Parameters: - --dropout: Dropout rate for regularization (default: 0.1) - --weight_decay: L2 regularization coefficient (default: 1e-4) - --patience: Early stopping patience epochs (default: 5) - --use_lr_scheduling: Enable cosine annealing (default: True) - --warmup_steps: Learning rate warmup steps (default: 100) - --val_split: Validation data fraction (default: 0.1) - --lr: Base learning rate (default: 1e-4)

Experimental Setup

Training Configuration

Architecture: Enhanced Diffusion-LM with comprehensive regularization Dataset: Shakespeare corpus (tiny_shakespeare) with train/val split GPU: Tesla V100 (16GB) - upgraded for faster iteration Git Commit: 85c25cf (mode-collapse-fixes branch)

Hyperparameters: - Epochs: 100 (with early stopping) - Batch Size: 8 (memory-optimized) - Embedding Dimension: 256 - Learning Rate: 5e-4 (increased from 1e-4) - Dropout: 0.2 (higher regularization) - Weight Decay: 1e-4 - Rounding Weight: 0.5 → 0.05 (dynamic decay) - Early Stopping Patience: 10 epochs - Warmup Steps: 50

Infrastructure Configuration

Deployment Config (shakespeare-training.yaml):

args: [
  "--train", "--epochs", "100", "--batch_size", "8", "--embed_dim", "256",
  "--use_learned_embeddings", "--init_from_pretrained",
  "--dropout", "0.2", "--weight_decay", "1e-4", "--patience", "10",
  "--use_lr_scheduling", "--warmup_steps", "50", "--lr", "5e-4", 
  "--rounding_weight", "0.5"
]

Experiment Tracking

Job Deployment

Job ID: 1956614562631385088
Deployment Time: 2025-07-22 20:30 UTC
Status: 🟡 RUNNING
Issue Tracker: Issue #20

Monitoring Commands:

# Check job status
uv run python deployment/monitor.py 1956614562631385088

# View training logs
uv run python deployment/monitor.py 1956614562631385088 --logs

# Full job details  
uv run python deployment/monitor.py 1956614562631385088 --full

Success Criteria

Training Stability: - ✅ Stable training curves without catastrophic loss spikes - ✅ Validation loss improves alongside training loss - ✅ Learning rate scheduling functioning correctly - ✅ Dynamic loss rebalancing working properly

Text Generation Quality: - ✅ Generated samples contain diverse vocabulary (not just punctuation) - ✅ Coherent Shakespeare-style phrases and sentence structures
- ✅ No mode collapse to high-frequency tokens

Architecture Validation: - ✅ Early stopping triggers appropriately based on validation metrics - ✅ Best checkpoint saved for optimal performance - ✅ All regularization components functioning correctly

Expected Outcomes

Based on the comprehensive improvements targeting each failure mode:

Stable Training: Learning rate scheduling + regularization should eliminate training instability observed in 1000-epoch run

Balanced Objectives: Dynamic rounding weight decay prevents token prediction from overwhelming diffusion learning

Quality Text Generation: Regularization + validation monitoring should produce diverse, coherent Shakespeare-style output

Efficient Training: Early stopping should find optimal performance around 20-50 epochs, avoiding overtraining

Related Work & Issues

Addresses: - Issue #18 (Mode Collapse Resolution - comprehensive analysis) - Issue #17 (1000-Epoch Extended Training - failure case)

Builds On: - Issue #14 (100-Epoch Training Experiment - functional baseline) - Issue #15 (Extended Training Strategy - initial approach) - Learned Rounding Implementation - architecture foundation

Implements: - PR #19 (Fix Mode Collapse: Comprehensive Training Improvements)

Current Status

Training Phase: 🟡 IN PROGRESS
Estimated Duration: 60-90 minutes on Tesla V100
Next Steps: Real-time monitoring → Completion analysis → Text generation validation

---

Experiment Timeline: - ✅ Problem Analysis: Mode collapse root cause identification - ✅ Solution Design: Comprehensive training improvements - ✅ Implementation: PR #19 with all fixes - ✅ Job Deployment: Training job submitted and running - ✅ Monitoring: Real-time training progress tracking - ✅ Results Analysis: Post-completion quality assessment - ✅ Text Generation: Sampling validation with trained model

Results

Status: ✅ COMPLETED - Training and sampling successful
Final Status: 🟡 PARTIAL SUCCESS - Technical success, quality issues remain

Training Performance

🎯 All Technical Objectives Met: - Training Stability: ✅ Perfect - no loss spikes, smooth convergence - Feature Validation: ✅ All new components (LR scheduling, regularization, early stopping) working correctly - Final Metrics: Train: 0.216, Val: 0.054 (best validation loss achieved) - Architecture Success: ✅ Comprehensive training pipeline fully functional

Generated Text Quality

❌ Mode Collapse Still Present (Different Pattern): - Previous Failure: Punctuation-only generation (, :)
- Current Issue: High-frequency word dominance (from, no) - Sample: "Well from with no no no from, from no no from from no no from I from from from no these no go how no no no from no from from"

Quality Assessment: - Token diversity improved vs punctuation-only collapse - Semantic content still minimal - ~70-80% of tokens are repetitive "from"/"no" - Some Shakespeare vocabulary present but overwhelmed

Key Insights

✅ Training Infrastructure Success: - Comprehensive regularization framework working - Learning rate scheduling prevented instability
- Dynamic loss rebalancing functioned as designed - Validation monitoring and early stopping ready

❌ Generation Quality Challenge: - Mode collapse shifted from punctuation to frequent words - Embedding space still vulnerable to token frequency bias - Need stronger diversity enforcement during sampling - Architecture changes may be required for semantic quality

Next Steps

Immediate: Token diversity penalties during generation Medium-term: Nucleus/top-k sampling, temperature scaling Architecture: Consider classifier-free guidance or alternative decoders