Reasoning trace (generated by Gemini)

Reasoning Trace Generation: According to the paper, they used the Google Gemini Flash Thinking API to generate reasoning traces. The process worked like this:

# Simplified version of their process
async def generate_reasoning_trace(question):
    # Using Google Gemini Flash Thinking API
    response = await gemini.flash_thinking(
        prompt=question,
        mode="experimental-1219"  # As mentioned in paper
    )
    
    # The response contains:
    reasoning_trace = response.thinking  # Step-by-step reasoning
    final_answer = response.answer
    
    return {
        'question': question,
        'reasoning_trace': reasoning_trace,
        'solution': final_answer
    }

Key aspects of the reasoning trace generation:

  1. They used Gemini's Flash Thinking mode which is specifically designed for step-by-step reasoning
  2. They extracted both the reasoning process and the final answer
  3. Each trace follows a structured format:
    • Breaking down the problem
    • Showing intermediate steps
    • Explaining key insights
    • Reaching a conclusion

The paper mentions that they got high-quality traces because Gemini's Flash Thinking mode is specifically optimized for showing detailed reasoning steps. They then used these traces for training their model to learn this reasoning pattern.