Seek in the Dark: Reasoning via Test-Time Instance-Level Policy Gradient
  in Latent Space
Authors: name: Hengli Li, name: Chenxi Li, name: Tong Wu, name: Xuekai Zhu, name: Yuxuan Wang, name: Zhaoxin Yu, name: Eric Hanchen Jiang, name: Song-Chun Zhu, name: Zixia Jia, name: Ying Nian Wu, name: Zilong Zheng

Reasoning ability, a core component of human intelligence, continues to pose
a significant challenge for Large Language Models (LLMs) in the pursuit of AGI.
Although model performance has improved under the training scaling law,
significant challenges remain, particularly with respect to training
algorithms, such as catastrophic forgetting, and the limited availability of
novel training data. As an alternative, test-time scaling enhances reasoning
performance by increasing test-time computation without parameter updating.
Unlike prior methods in this paradigm focused on token space, we propose
leveraging latent space for more effective reasoning and better adherence to
the test-time scaling law. We introduce LatentSeek, a novel framework that
enhances LLM reasoning through Test-Time Instance-level Adaptation (TTIA)
within the model's latent space. Specifically, LatentSeek leverages policy
gradient to iteratively update latent representations, guided by self-generated
reward signals. LatentSeek is evaluated on a range of reasoning benchmarks,
including GSM8K, MATH-500, and AIME2024, across multiple LLM architectures.
Results show that LatentSeek consistently outperforms strong baselines, such as
Chain-of-Thought prompting and fine-tuning-based methods. Furthermore, our
analysis demonstrates that LatentSeek is highly efficient, typically converging
within a few iterations for problems of average complexity, while also
benefiting from additional iterations, thereby highlighting the potential of
test-time scaling in the latent space. These findings position LatentSeek as a
lightweight, scalable, and effective solution for enhancing the reasoning
capabilities of LLMs.