HyLaR: Hybrid Latent Reasoning with Decoupled Policy Optimization

Chain-of-Thought (CoT) reasoning significantly elevates thecomplex problem-solving capabilities of multimodal large language mod-els (MLLMs). However, adapting CoT to vision typically discretizes sig-nals to fit LLM inputs, causing early semantic collapse and discard-ing fine-grained details. While external tools can mitigate this, theyintroduce a rigid bottleneck, confining reasoning to predefined oper-ations. Although recent latent reasoning paradigms internalize visualstates to overcome these limitations, optimizing the resulting hybriddiscrete-continuous action space remains challenging. In this work, wepropose HyLaR (Hybrid Latent Reasoning), a framework that seamlesslyinterleaves discrete text generation with continuous visual latent rep-resentations. Specifically, following an initial cold-start supervised fine-tuning (SFT), we introduce DePO (Decoupled Policy Optimization) toenable effective reinforcement learning within this hybrid space. DePOdecomposes the policy gradient objective, applying independent trust-region constraints to the textual and latent components, alongside anexact closed-form von Mises-Fisher (vMF) KL regularizer. Extensive ex-periments demonstrate that HyLaR outperforms standard MLLMs andstate-of-the-art latent reasoning approaches across fine-grained percep-tion and general multimodal understanding benchmarks. Code is avail-able at https://github.com/EthenCheng/HyLaR.