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This paper analyzes the soft thinking capabilities of LLM and identifies a problem: its single-threaded nature prevents it from exploring diverse inference paths. To address this issue, dubbed "greedy pitfall," we propose stochastic soft thinking, specifically leveraging the Gumbel-Softmax trick to enhance soft thinking performance through randomness.
Takeaways, Limitations
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Takeaways:
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A new discovery: LLM's Soft Thinking operates in a single-threaded manner.
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Proposing Stochastic Soft Thinking to solve the Greedy Pitfall problem and demonstrating its performance improvement.
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Demonstrating the effectiveness of Stochastic Soft Thinking using the Gumbel-Softmax trick.
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Stochastic Soft Thinking has a more powerful exploration ability than Chain-of-Thought (COT).
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Developing a deeper understanding of continuous reasoning and laying the foundation for improving soft thinking through reinforcement learning.
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Limitations:
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Further research is needed on the internal working mechanisms of specific Soft Thinking.
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Further experiments are needed to determine the optimal degree of randomness in Stochastic Soft Thinking.
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The generalizability of the proposed methodology to other types of LLMs and diverse problem domains needs to be verified.
