Synthetic Data Generation

What It Is

Using AI to generate training data variations, expanding datasets without manual effort.

The Ralph Connection

This is where Ralph's iterative loop concept applies to training: - Loop generates prompt/response pairs - Each iteration creates variations - Quality filtering removes bad examples - Feed filtered data into training

Implementation for LARS

Variation Generator Loop

while not satisfied:
    # Generate variations of existing examples
    new_examples = claude.generate_variations(seed_examples)

    # Quality filter
    filtered = quality_check(new_examples)

    # Add to training set
    training_data.extend(filtered)

    # Check if we have enough diversity
    if diversity_score(training_data) > threshold:
        satisfied = True

Types of Variations

1. Paraphrasing: Same meaning, different words
2. Complexity scaling: Simple → detailed versions
3. Context injection: Add Nexus-specific context
4. Edge cases: Unusual but valid examples
5. Adversarial: Trick questions to improve robustness

Quality Filtering

• Semantic similarity to originals (not too far)
• Grammar/coherence checks
• Factual accuracy validation
• Deduplication

Estimated Impact

• Can 10x dataset size with minimal effort
• More diverse data = better generalization
• Reduces overfitting to specific phrasings