SLPO: Scaling Latent Reasoning via a Surrogate Policy
Reinforcement learning has enabled test-time scaling in explicit Chain-of-Thought reasoners but is computationally expensive. Latent reasoning uses continuous vectors for intermediate computation, matching explicit CoT efficiency but lacking RL training. This paper introduces Surrogate Latent Policy Optimization (SLPO) to apply outcome-reward RL to autoregressive latent reasoners via a surrogate policy density for trajectory-level credit assignment and a correctness-supervised stopping head for variable-horizon policy. SLPO improves Pass@k and allocates longer computation to harder instances.
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[Submitted on 22 Jul 2026]
Title:SLPO: Scaling Latent Reasoning via a Surrogate Policy
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Abstract: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 every intermediate step must be decoded as a language token. 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 budgets, so outcome rewards cannot elicit latent test-time scaling. We introduce Surrogate Latent Policy Optimization (SLPO) to bring outcome-reward RL to autoregressive latent reasoners: an empirical surrogate policy density over latent transitions for trajectory-level credit assignment, and a correctness-supervised stopping head that outcome-reward optimization refines into a variable-horizon policy. Across continuous and soft thinking settings, SLPO improves Pass@$k$ under parallel sampling and allocates longer latent computation to harder instances with higher deterministic accuracy.
Subjects:
Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
Cite as: arXiv:2607.19691 [cs.CL]
(or arXiv:2607.19691v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2607.19691
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Runyang You [view email] [v1] Wed, 22 Jul 2026 02:45:08 UTC (2,983 KB)
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