Understanding the Challenges of Large Language Models in Mathematics
Large Language Models (LLMs) struggle with mathematical reasoning, which includes tasks like understanding math concepts, solving problems, and making logical deductions. While there are methods to improve LLMs’ math skills, the potential of state transition in enhancing their reasoning abilities is often overlooked.
Current Approaches to Improve LLMs
Existing methods focus on training models like GPT, LLaMA, and MetaMath using large-scale mathematical prompts for better problem-solving. Techniques like Chain of Thought (CoT) and Best-of-N aim to maximize LLM performance during inference. Other methods, such as Monte Carlo Tree Search and Process Reward Model, break down problems into steps and provide rewards, but they often lack efficiency and adaptability.
Introducing Kwai-STaR: A New Framework
Kwai-STaR is a framework designed to convert general LLMs into state transition reasoners, enabling systematic problem-solving through state transitions. Developed by researchers from Tsinghua University and Kuaishou Technology, it involves:
- Defining a state space for problem-solving.
- Creating a state-transition dataset with correct and verified cases.
- Training LLMs using a two-stage curriculum for efficiency.
The training strategy includes a fundamental stage for simple problems and an advanced stage for complex cases, enhancing the model’s proficiency. Kwai-DStar, tested on benchmarks like GSM8K, demonstrated impressive performance and efficiency with simpler inference processes.
The Future of Kwai-STaR
Kwai-DStar enhances traditional LLMs, improving their ability to solve mathematical problems. While it has proven effective in math, researchers are exploring its application in more diverse scenarios to validate its broader potential.
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