Hybrid Latent Reasoning via Reinforcement Learning
Authors: name: Zhenrui Yue, name: Bowen Jin, name: Huimin Zeng, name: Honglei Zhuang, name: Zhen Qin, name: Jinsung Yoon, name: Lanyu Shang, name: Jiawei Han, name: Dong Wang

Recent advances in large language models (LLMs) have introduced latent
reasoning as a promising alternative to autoregressive reasoning. By performing
internal computation with hidden states from previous steps, latent reasoning
benefit from more informative features rather than sampling a discrete
chain-of-thought (CoT) path. Yet latent reasoning approaches are often
incompatible with LLMs, as their continuous paradigm conflicts with the
discrete nature of autoregressive generation. Moreover, these methods rely on
CoT traces for training and thus fail to exploit the inherent reasoning
patterns of LLMs. In this work, we explore latent reasoning by leveraging the
intrinsic capabilities of LLMs via reinforcement learning (RL). To this end, we
introduce hybrid reasoning policy optimization (HRPO), an RL-based hybrid
latent reasoning approach that (1) integrates prior hidden states into sampled
tokens with a learnable gating mechanism, and (2) initializes training with
predominantly token embeddings while progressively incorporating more hidden
features. This design maintains LLMs' generative capabilities and incentivizes
hybrid reasoning using both discrete and continuous representations. In
addition, the hybrid HRPO introduces stochasticity into latent reasoning via
token sampling, thereby enabling RL-based optimization without requiring CoT
trajectories. Extensive evaluations across diverse benchmarks show that HRPO
outperforms prior methods in both knowledge- and reasoning-intensive tasks.
Furthermore, HRPO-trained LLMs remain interpretable and exhibit intriguing
behaviors like cross-lingual patterns and shorter completion lengths,
highlighting the potential of our RL-based approach and offer insights for
future work in latent reasoning.