Real-time example retrieval makes LLMs better at mathematical reasoning, one step at a time.
BoostStep enhances mathematical reasoning in LLMs by refining in-context learning to step-level granularity, providing real-time guidance during complex problem-solving.
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https://arxiv.org/abs/2501.03226
Original Problem 🤔:
LLMs struggle with mathematical reasoning due to inaccuracies in individual reasoning steps, despite understanding the overall problem-solving approach. Traditional in-context learning provides examples at the problem level, which often lacks relevant guidance for specific challenging steps.
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Solution in this Paper 🛠️:
→ BoostStep introduces step-level in-context learning, breaking down problems into atomic reasoning steps
→ The system employs a "first-try" strategy where the model attempts each step before receiving guidance
→ A specialized step-level example bank is constructed based on reasoning content rather than grammatical separation
→ Similar steps are retrieved and provided as real-time guidance during the reasoning process
→ The method integrates seamlessly with Monte Carlo Tree Search for both reasoning and verification phases
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Key Insights 🔍:
→ Different problems often share similar key reasoning steps, even when the problems themselves are dissimilar
→ Step-level guidance reduces dependency on overall problem similarity
→ Real-time example retrieval significantly improves reasoning accuracy
→ Combining step-level guidance with MCTS enhances both generation and verification capabilities
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Results 📊:
→ Improved GPT-4o performance by 3.6% across mathematical benchmarks
→ Enhanced Qwen2.5-Math-72B performance by 2.0%
→ Achieved 7.5% improvement when combined with MCTS
→ Demonstrated consistent gains even on dissimilar problem types
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