### Exploration Experiments: Varying CoT Length, Style, and Categories

Source: https://context7.com/parkjumyung/skills/llms.txt

This Python code snippet demonstrates quick experiments to explore the impact of Chain-of-Thought (CoT) length, reasoning styles, and problem categories on model performance. It evaluates the model with different CoT lengths, various reasoning formats, and across different problem types to gather initial insights.

```python
# Quick experiment: vary CoT length
for length in [0, 1, 3, 5, 10]:
    results[length] = evaluate(model, prompt_with_cot_length(length))

# Quick experiment: different reasoning styles
for style in ["step-by-step", "bullet-points", "natural", "formal"]:
    results[style] = evaluate(model, prompt_with_style(style))

# Quick experiment: problem categories
for category in problem_categories:
    results[category] = evaluate_cot_vs_direct(model, category)
```

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