Kun Liang

Learning Latent Reasoning Traces for Scalar Reward Models End-to-End

LatentRM is a reward modeling framework that learns intermediate reasoning traces as discrete latent variables to explicitly maximize the likelihood of downstream scalar rewards through on-policy optimization of the latent reasoning space end-to-end.