SLPO: Scaling Latent Reasoning via a Surrogate Policy

• URL: http://arxiv.org/abs/2607.19691v2
• Date: Mon, 27 Jul 2026 00:25:39 GMT
• Title: SLPO: Scaling Latent Reasoning via a Surrogate Policy
• Abstract summary: Reinforcement learning with verifiable rewards has become the predominant recipe for eliciting test-time scaling in explicit Chain-of-Thought reasoners.<n>Latent reasoning instead carries intermediate computation as continuous vectors.<n>We introduce Surrogate Latent Policy Optimization to bring outcome-reward RL to autoregressive latent reasoners.
• Score: 25.465651511203337
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Think Wider: Mitigating Latent Rank Collapse in Implicit Chain-of-Thought Reasoning [3.216436532980238]
  Chain-of-thought (CoT) reasoning improves the reasoning ability of large language models by introducing intermediate computation.<n>However, explicit rationales increase decoding length, latency, and context cost.<n>Implicit CoT offers a more efficient alternative by moving intermediate reasoning into continuous latent states.
  arXiv  Detail & Related papers  (2026-09-07T12:18:51Z)
• Latent Thought Flow: Efficient Latent Reasoning in Large Language Models [23.52673819075993]
  We propose Latent Thought Flow (LTF), which models reasoning as variable-length continuous trajectories and trains a sampler to match a reward-induced posterior over answer quality and computation cost.<n>Experiments under finetuning and transfer learning settings show that LTF outperforms explicit CoT and latent reasoning baselines, improving accuracy by 9.5% and reducing reasoning length by 27.2% on average.
  arXiv  Detail & Related papers  (2026-06-15T05:02:46Z)
• Demystifying Hidden-State Recurrence: Switchable Latent Reasoning with On-Policy Reinforcement Learning [38.056339518861506]
  We propose SWITCH, a switchable latent reasoning framework.<n>The model emits swi> to enter latent mode and /swi> to exit.<n>We show that SWITCH consistently outperforms prior hidden-state-recurrence latent reasoning approaches.
  arXiv  Detail & Related papers  (2026-06-11T09:33:40Z)
• Selective Latent Thinking: Adaptive Compression of LLM Reasoning Chains [9.979461886634022]
  Explicit chain-of-thought (CoT) reasoning substantially improves the reasoning ability of large language models.<n>Existing latent reasoning methods treat reasoning as uniformly compressible, causing precision-critical intermediate steps to be overly compressed.<n>We propose Selective Latent Thinking (SLT), a framework that selectively compresses redundant reasoning spans into latent representations.
  arXiv  Detail & Related papers  (2026-05-25T11:57:09Z)
• From $\boldsymbol{\logπ}$ to $\boldsymbolπ$: Taming Divergence in Soft Clipping via Bilateral Decoupled Decay of Probability Gradient Weight [6.07556923512707]
  Reinforcement Learning with Verifiable Rewards (RLVR) has catalyzed a leap in Large Language Model (LLM) reasoning, yet its optimization dynamics remain fragile.<n>Standard algorithms like GRPO enforce stability via hard clipping'', which inadvertently stifles exploration by discarding gradients of tokens outside the trust region.<n>We propose Decoupled Gradient Policy Optimization (DGPO), which employs a decoupled decay mechanism based on importance sampling ratios.
  arXiv  Detail & Related papers  (2026-03-15T14:00:48Z)
• Improving Search Agent with One Line of Code [68.58667107354253]
  Tool-based Agentic Reinforcement Learning (TARL) has emerged as a promising paradigm for training search agents to interact with external tools.<n>We propose textbfSearch textbfAgent textbfPolicy textbfOptimization (textbfSAPO), which stabilizes training via a conditional token-level KL constraint.
  arXiv  Detail & Related papers  (2026-03-10T04:07:39Z)
• Stepwise Penalization for Length-Efficient Chain-of-Thought Reasoning [66.22060690012512]
  Large reasoning models improve with more test-time computation, but often overthink, producing unnecessarily long chains-of-thought that raise cost without improving accuracy.<n>We propose Step-wise Adaptive Penalization (SWAP), a fine-grained framework that allocates length reduction across steps based on intrinsic contribution.
  arXiv  Detail & Related papers  (2026-02-27T20:23:59Z)
• WS-GRPO: Weakly-Supervised Group-Relative Policy Optimization for Rollout-Efficient Reasoning [67.45237332694025]
  Group Relative Policy Optimization is effective for training language models on complex reasoning.<n>We propose Weakly Supervised GRPO, which improves rollout efficiency by converting terminal rewards into correctness-aware guidance.
  arXiv  Detail & Related papers  (2026-02-19T02:43:35Z)
• Step Potential Advantage Estimation: Harnessing Intermediate Confidence and Correctness for Efficient Mathematical Reasoning [25.562101968892833]
  Reinforcement Learning with Verifiable Rewards (RLVR) elicits long chain-of-thought reasoning in large language models (LLMs)<n>Existing approaches improve RLVR via token-level entropy or sequence-level length control, but lack a semantically grounded, step-level measure of reasoning progress.<n>We propose Step Potential Advantage Estimation (SPAE), a fine-grained credit assignment method that amplifies potential gains, penalizes potential drops, and applies penalty after potential saturates to encourage timely termination.
  arXiv  Detail & Related papers  (2026-01-07T11:36:01Z)
• Arbitrage: Efficient Reasoning via Advantage-Aware Speculation [71.45710345765528]
  Speculative Decoding accelerates inference by employing a fast but inaccurate draft model to autoregressively propose tokens.<n>But due to unnecessary rejections caused by token mismatches in semantically equivalent steps, traditional token-level Speculative Decoding struggles in reasoning tasks.<n>We propose Arbitrage, a novel step-level speculative generation framework that routes generation dynamically based on the relative advantage between draft and target models.
  arXiv  Detail & Related papers  (2025-12-04T17:50:53Z)
• Latent Chain-of-Thought for Visual Reasoning [53.541579327424046]
  Chain-of-thought (CoT) reasoning is critical for improving the interpretability and reliability of Large Vision-Language Models (LVLMs)<n>We reformulate reasoning in LVLMs as posterior inference and propose a scalable training algorithm based on amortized variational inference.<n>We empirically demonstrate that the proposed method enhances the state-of-the-art LVLMs on seven reasoning benchmarks.
  arXiv  Detail & Related papers  (2025-10-27T23:10:06Z)
• Fractured Chain-of-Thought Reasoning [61.647243580650446]
  We introduce Fractured Sampling, a unified inference-time strategy that interpolates between full CoT and solution-only sampling.<n>We show that Fractured Sampling consistently achieves superior accuracy-cost trade-offs, yielding steep log-linear scaling gains in Pass@k versus token budget.
  arXiv  Detail & Related papers  (2025-05-19T11:30:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.