Large Language Models (LLMs) struggle with complex reasoning due to limited diversity and inefficient search. We propose Soft Reasoning, an embedding-based search framework that optimises the embedding of the first token to guide generation. It combines (1) embedding perturbation for controlled exploration and (2) Bayesian optimisation to refine embeddings via a verifier-guided objective, balancing exploration and exploitation. This approach improves reasoning accuracy and coherence while avoiding reliance on heuristic search. Experiments demonstrate superior correctness with minimal computation, making it a scalable, model-agnostic solution.
View on arXiv@article{zhu2025_2505.24688,
title={ Soft Reasoning: Navigating Solution Spaces in Large Language Models through Controlled Embedding Exploration },
author={ Qinglin Zhu and Runcong Zhao and Hanqi Yan and Yulan He and Yudong Chen and Lin Gui },
journal={arXiv preprint arXiv:2505.24688},
year={ 2025 }
}