#looped-language-models News & Analysis

Stabilizing Recurrent Dynamics for Test-Time Scalable Latent Reasoning in Looped Language Models

Researchers propose STARS, a training framework that stabilizes Looped Language Models (LoopLMs) to enable reliable test-time scaling through latent reasoning. The method uses Jacobian Spectral Radius Regularization to constrain neural states toward stable fixed points, addressing a critical problem where model performance peaks then collapses with increased recurrence depth.