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AlphaMath Almost Zero: Process Supervision Without Process

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Process supervision for math reasoning without human annotations

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Type
Open Source
Visit Website GitHub (40 stars)

About AlphaMath Almost Zero: Process Supervision Without Process

AlphaMath is a framework designed to enhance the mathematical reasoning capabilities of large language models (LLMs) without relying on costly process annotations from humans or GPT-4. It leverages Monte Carlo Tree Search (MCTS) to automatically generate process supervision and step-level evaluation signals by integrating a value model with the LLM. The framework also introduces an efficient inference strategy called step-level beam search, where the value model guides the policy model toward more effective reasoning paths. Experimental results on in-domain and out-of-domain datasets show that AlphaMath achieves comparable or superior results to previous state-of-the-art methods, even without explicit process supervision. The paper was accepted at NeurIPS 2024.

Key Features

Monte Carlo Tree Search (MCTS) for automatic process supervision generation
Integration of a value model with the LLM to produce step-level evaluation signals
Step-level beam search inference strategy for efficient reasoning path selection
Eliminates the need for expensive human or GPT-4 process annotations
Achieves state-of-the-art mathematical reasoning performance without supervised process data

Pros & Cons

Pros
  • Bypasses the need for costly and labor-intensive process annotations from humans or GPT-4
  • Automatically generates step-level supervision signals during training
  • Efficient inference via step-level beam search guided by the value model
  • Achieves competitive or superior results compared to methods using external supervision
Cons
  • Primarily focused on mathematical reasoning, not general tasks
  • Requires significant computational resources for Monte Carlo Tree Search
  • May not be directly applicable to non-symbolic or open-ended reasoning problems

Best For

Enhancing mathematical reasoning in large language modelsAutomatic generation of process supervision for multi-step reasoning tasksImproving LLM performance on complex symbolic and multi-step math problems

FAQ

What is AlphaMath?
AlphaMath is a framework that uses Monte Carlo Tree Search to provide process supervision for large language models in mathematical reasoning, without needing human or GPT-4 annotation.
How does AlphaMath generate process supervision?
It integrates a value model with the LLM within Monte Carlo Tree Search to automatically produce step-level evaluation signals and process supervision.
Does AlphaMath require human-annotated data?
No, it bypasses the need for process annotations from humans or GPT-4, using only the MCTS framework and a pretrained LLM.
What inference strategy does AlphaMath propose?
It proposes step-level beam search, where the value model helps the policy model explore more effective reasoning paths rather than relying solely on prior probabilities.