#computational-depth News & Analysis

Latent Reasoning in TRMs is Secretly a Policy Improvement Operator

Researchers demonstrate that latent reasoning in transformer models functions as a policy improvement operator rather than simply adding computational depth. By applying reinforcement learning and diffusion training methods, they achieve 18x reduction in forward passes while maintaining performance, revealing how recursive steps either contribute meaningfully or become dead compute.