arXiv:2604.17892v4 Announce Type: replace-cross Abstract: Recently, latent reasoning has been introduced into large language models (LLMs) to leverage rich information within a continuous space. However, without stochastic sampling, these methods inevitably collapse to deterministic inference, failing to discover diverse reasoning paths. To bridge the gap, we inject controllable stochasticity into latent reasoning via Gumbel-Softmax, restoring LLMs' exploratory capacity and enhancing their compatibility with Reinforcement Learning (RL). Building on this, we propose \textbf{\underline{L}}atent
The paper addresses current limitations in latent reasoning for LLMs by introducing stochasticity, a critical next step in advancing their exploratory capabilities for complex tasks.
Improving latent reasoning and stochastic sampling directly enhances LLMs' ability to discover diverse and more effective solutions, which is crucial for their application in complex problem-solving and autonomous systems.
LLMs can move beyond deterministic inference in latent reasoning, gaining a more robust capacity for exploration and compatibility with reinforcement learning paradigms.
- · AI developers
- · LLM applications
- · Reinforcement Learning research
- · Traditional deterministic reasoning methods
Increased performance and adaptability of LLMs in tasks requiring complex planning and decision-making.
Accelerated development of more sophisticated AI agents capable of handling real-world ambiguity and dynamic environments.
Potential for LLMs to tackle previously intractable computational problems through more effective exploration of solution spaces.
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Read at arXiv cs.AI