Seek in the Dark: Reasoning via Test-Time Instance-Level Policy Gradient in Latent Space

Understand

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.