ReHoPER: Receding-Horizon Planning for Enhanced Reasoning
Saeed Ahmadnia, Cornelia Caragea
Abstract
We propose ReHoPER, an inference-only, zero-shot method that improves large language models' reasoning by generating and answering intermediate questions along multiple paths before the final answer. It iteratively plans a horizon of candidate intermediate questions, selects one to answer, and replans from the updated history. ReHoPER is task-agnostic, using the same generic instructions across datasets and models without labeled data or task-specific prompt design. Across multiple datasets, including iLLC, a new controlled benchmark for compositional reasoning, ReHoPER outperforms strong baselines, with the largest gains in the most compositional settings. Our implementation and the iLLC generator are publicly available to support future work.
Create a lesson
Related papers
KaliBench: A Fine-Grained Benchmark for Cybersecurity Tool Use on Kali Linux with Runtime-Free Verifiable Rewards
Pengfei Li, Naufal Suryanto, Sicheng Zhang et al.
Hierarchical Continuous Diffusion Language Models
Hui Ren, Zihan Li, Chang Liu et al.
AutoCompact: Learning When to Compact Context in Long-Horizon Coding Agents
Xuan Zhang, Longtao Zheng, Cunxiao Du et al.
From Knowledge Access to Source Learning: Developing Source-Specific Competence
Lucheng Fu, Kejing Xia, Yiyang Wang et al.
Keyword Harnesses Fail Open: A Cheap Diagnostic Ladder for Tool-Use Claims in Small Language Models
Juan S. Santillana
Argo-Bench: Evaluating Data Agents on Enterprise-Scale Workflows
Gabriel Tomitsuka, Arman Raayatsanati, Emma Xing et al.