BFS-PO: Best-First Search for Large Reasoning Models

• URL: http://arxiv.org/abs/2602.14917v1
• Date: Mon, 16 Feb 2026 16:53:41 GMT
• Title: BFS-PO: Best-First Search for Large Reasoning Models
• Authors: Fiorenzo Parascandolo, Wenhui Tan, Enver Sangineto, Ruihua Song, Rita Cucchiara,
• Abstract summary: Large Reasoning Models (LRMs) such as OpenAI o1 and DeepSeek-R1 have shown excellent performance in reasoning tasks.<n>This has led to a significant increase of computational costs and the generation of verbose output.<n>In this paper, we propose BFS-PO, an RL algorithm which alleviates this problem using a Best-First Search exploration strategy.
• Score: 48.89264625477105
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Reasoning Models (LRMs) such as OpenAI o1 and DeepSeek-R1 have shown excellent performance in reasoning tasks using long reasoning chains. However, this has also led to a significant increase of computational costs and the generation of verbose output, a phenomenon known as overthinking. The tendency to overthinking is often exacerbated by Reinforcement Learning (RL) algorithms such as GRPO/DAPO. In this paper, we propose BFS-PO, an RL algorithm which alleviates this problem using a Best-First Search exploration strategy. Specifically, BFS-PO looks for the shortest correct answer using a backtracking mechanism based on maximum entropy nodes. By generating progressively shorter responses during training, BFS-PO learns to produce concise reasoning chains. Using different benchmarks and base LRMs, we show that BFS-PO can simultaneously increase the LRM accuracy and shorten its answers.

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