Looped language models (LoopLMs) perform iterative latent computation to refine internal representations, offering an alternative to explicit chain-of-thought (CoT) reasoning.
However, existing reinforcement learning (RL) paradigms primarily target output tokens, creating a structural mismatch with looped architectures whose reasoning unfolds implicitly.
In this work, we propose LoopRPT, a reinforcement pre-training framework tailored for LoopLMs.
By reframing next-token prediction as a next-token reasoning task, LoopRPT assigns reinforcement signals directly to latent steps using an EMA teacher reference and noisy latent rollouts.
This formulation enables RL to directly shape intermediate representations, compressing effective reasoning into fewer iterations.
We instantiate LoopRPT on Ouro across multiple model scales.
Results show that LoopRPT improves per-step representation quality and achieves Pareto dominance in accuracy-computation trade-offs.
Large gains on hard tokens indicate that LoopRPT enhances early-stage reasoning rather than merely encouraging premature exits.
Our findings highlight reinforcement pre-training as a principled paradigm for learning efficient latent reasoning in LoopLMs.