🚀 DTS: A Candidate for the Best Parallel Reasoning in LLMs

Decoding Tree Sketching (DTS) is introduced as a novel approach to parallel reasoning in large language models, addressing the limitations of existing methods such as DeepConf/Self-Consistency, beam search, and majority voting, which tend to waste resources by parallelizing outputs instead of decisions. DTS innovatively restructures reasoning by focusing on decision points and branching only when semantically diverse options are present, creating a compact and information-dense reasoning tree. It leverages uncertainty signals to guide branching decisions, prioritizing short and reliable reasoning paths to avoid errors common in lengthy reasoning processes. DTS offers parallel exploration with bounded complexity and scalable inference without requiring additional training, making it easily integrable into existing decoding frameworks and applicable in various reasoning tasks, demonstrating improved accuracy and reduced redundancy in benchmarks.