SLPO: Scaling Latent Reasoning via a Surrogate Policy
Reinforcement learning with verifiable rewards has become the predominant recipe for eliciting test-time scaling in explicit Chain-of-Thought reasoners. Yet this scaling path remains computationally costly, since intermediate reasoning must be externalized as natural-language tokens. Latent reasoning instead carries intermediate computation as continuous vectors and already matches or surpasses explicit CoT at far shorter horizons. Despite this promise, latent reasoners remain largely imitation-bound, while explicit CoT has already moved past imitation via outcome-reward RL. Latent trajectories lack a tractable per-step likelihood and an adaptive stopping interface under fixed thinking bu...
| Authors | Runyang You, Zhiyuan Liu, Yongqi Li et al. |
|---|---|
| Primary category | cs.CL |
| Published | 22 July 2026 |
| Last updated at source | 3 October 2026 |
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