  With recent advancements in large language models, methods like
chain-of-thought prompting to elicit reasoning chains have been shown to
improve results on reasoning tasks. However, tasks that require multiple steps
of reasoning still pose significant challenges to state-of-the-art models.
Drawing inspiration from the beam search algorithm, we propose PathFinder, a
tree-search-based reasoning path generation approach. It enhances diverse
branching and multi-hop reasoning through the integration of dynamic decoding,
enabled by varying sampling methods and parameters. Using constrained
reasoning, PathFinder integrates novel quality constraints, pruning, and
exploration methods to enhance the efficiency and the quality of generation.
Moreover, it includes scoring and ranking features to improve candidate
selection. Our approach outperforms competitive baselines on three complex
arithmetic and commonsense reasoning tasks by 6% on average. Our model
generalizes well to longer, unseen reasoning chains, reflecting similar
complexities to beam search with large branching factors.
