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About this paper
Xuyang Zhao, Liting Zhang, Zichen Xu, Yong Chen, Wenjia Zeng, Shiwan Zhao, Qicheng Li
Latent Thought Credit (LTC) introduces a hierarchical credit-assignment framework for large language models that perform reasoning in continuous latent spaces. It enhances the reliability of latent thought evaluation by averaging rewards from multiple downstream answers, achieving improved performance on mathematical reasoning and STEM benchmarks.
Key insights
- Averaging rewards from multiple answers significantly reduces the noise in estimating the expected utility of a latent thought, providing a more stable optimization signal.
- A hierarchical credit assignment framework, distinguishing between latent thought utility and discrete answer correctness, is more effective than flat credit assignment.
- Incorporating stochasticity (Gumbel noise) for latent thought exploration and an auxiliary thought-matching objective are crucial for learning and internalizing effective reasoning patterns.
Summary by alphaXiv.
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