LaDiR: Latent Diffusion Enhances LLMs for Text Reasoning

Understand

Large Language Models (LLMs) demonstrate their reasoning ability through chain-of-thought (CoT) generation.

• However, LLM's autoregressive decoding may limit the ability to revisit and refine earlier tokens in a holistic manner, which can also lead to inefficient exploration for diverse solutions.
• In this paper, we propose LaDiR} (Latent Diffusion Reasoner), a novel reasoning framework that unifies the expressiveness of continuous latent representation with the iterative refinement capabilities of latent diffusion models for an existing LLM.
• We first construct a structured latent reasoning space using a Variational Autoencoder (VAE) that encodes text reasoning steps into blocks of thought tokens, preserving semantic information and interpretability while offering compact but expressive representations.