Soft Reasoning: Navigating Solution Spaces in Large Language Models through Controlled Embedding Exploration
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909785319800832 |
|---|---|
| author | Zhu, Qinglin Zhao, Runcong Yan, Hanqi He, Yulan Chen, Yudong Gui, Lin |
| author_facet | Zhu, Qinglin Zhao, Runcong Yan, Hanqi He, Yulan Chen, Yudong Gui, Lin |
| contents | 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. The code is released at https://github.com/alickzhu/Soft-Reasoning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_24688 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Soft Reasoning: Navigating Solution Spaces in Large Language Models through Controlled Embedding Exploration Zhu, Qinglin Zhao, Runcong Yan, Hanqi He, Yulan Chen, Yudong Gui, Lin Computation and Language 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. The code is released at https://github.com/alickzhu/Soft-Reasoning. |
| title | Soft Reasoning: Navigating Solution Spaces in Large Language Models through Controlled Embedding Exploration |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2505.24688 |