ReHoPER: Receding-Horizon Planning for Enhanced Reasoning

ReHoPER is an inference‑only, zero‑shot method that enhances large language models’ reasoning by generating and answering intermediate questions along multiple paths before producing a final answer. It plans a horizon of candidate intermediate questions, selects one to answer, and replans based on the updated history. The approach is task‑agnostic, using generic instructions across datasets and models without labeled data or task‑specific prompt design, and it outperforms strong baselines on several datasets, notably achieving the largest gains on the new iLLC benchmark for compositional reasoning.

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• llms
• benchmarks