[2603.24780] Transformers in the Dark: Navigating Unknown Search Spaces via Bandit Feedback

Computer Science > Machine Learning arXiv:2603.24780 (cs) [Submitted on 25 Mar 2026] Title:Transformers in the Dark: Navigating Unknown Search Spaces via Bandit Feedback Authors:Jungtaek Kim, Thomas Zeng, Ziqian Lin, Minjae Lee, Chungpa Lee, Jy-yong Sohn, Hyung Il Koo, Kangwook Lee View a PDF of the paper titled Transformers in the Dark: Navigating Unknown Search Spaces via Bandit Feedback, by Jungtaek Kim and 7 other authors View PDF HTML (experimental) Abstract:Effective problem solving with Large Language Models (LLMs) can be enhanced when they are paired with external search algorithms. By viewing the space of diverse ideas and their follow-up possibilities as a tree structure, the search algorithm can navigate such a search space and guide the LLM toward better solutions more efficiently. While the search algorithm enables an effective balance between exploitation and exploration of a tree-structured space, the need for an external component can complicate the overall problem-solving process. We therefore pose the following question: Can LLMs or their underlying Transformer architectures approximate a search algorithm? To answer this question, we first introduce a simplified framework in which tree extensions and feedback signals are externally specified, allowing for controlled evaluation of search capabilities. We call this setting unknown tree search with bandit feedback. Within this setting, we show that Transformers are theoretically expressive enough to implemen...