mlsanityData to 7 Oct 2026

ReHoPER: Receding-Horizon Planning for Enhanced Reasoning

arXiv 2610.00940 ↗First posted 1 Oct 2026cs.CL cs.AI cs.LGcited by 0

On the map: LLM reasoning → Test-time compute and efficient thinking also fits: Knowledge- and tool-augmented reasoning

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Test-time compute and efficient thinking, one of 17 directions in LLM reasoning. Builds on 29 of its 35 references that are in the map. First posted on arXiv 1 Oct 2026.

Builds on

29 of 35 references.

Closest ancestors · 5

Same direction, newest first

  1. 2025gpt-oss-120b & gpt-oss-20b Model Card8 Aug
  2. 2025Think Twice: Enhancing LLM Reasoning by Scaling Multi-round Test-time Thinking25 Mar
  3. 2024Self-Discover: Large Language Models Self-Compose Reasoning Structures6 Feb
  4. 2023Tree of Thoughts: Deliberate Problem Solving with Large Language Models17 May
  5. 2023Self-Refine: Iterative Refinement with Self-Feedback30 Mar

Other references · 24

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  3. 2022Training language models to follow instructions with human feedbackcited by 15,712
  4. 2024The Llama 3 Herd of Modelscited by 14,192
  5. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  6. 2025Qwen3 Technical Reportcited by 8,195
  7. 2023Efficient Memory Management for Large Language Model Serving with PagedAttentioncited by 5,767
  8. 2022Self-Consistency Improves Chain of Thought Reasoning in Language Modelscited by 5,601
  9. 2022Large Language Models are Zero-Shot Reasonerscited by 4,323
  10. 2021Finetuned Language Models Are Zero-Shot Learnerscited by 3,747
  11. 2019HuggingFace's Transformers: State-of-the-art Natural Language Processingcited by 2,201
  12. 2025Gemma 3 Technical Reportcited by 1,686
12 more
  1. 2022Least-to-Most Prompting Enables Complex Reasoning in Large Language Modelscited by 1,397
  2. 2023AWQ: Activation-aware Weight Quantization for LLM Compression and Accelerationcited by 1,234
  3. 2022Measuring and Narrowing the Compositionality Gap in Language Modelscited by 1,054
  4. 2024Phi-4 Technical Reportcited by 675
  5. 2023Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Modelscited by 550
  6. 2021Recent Advances in Natural Language Processing via Large Pre-Trained Language Models: A Surveycited by 457
  7. 2023Chain-of-Verification Reduces Hallucination in Large Language Modelscited by 317
  8. 2023MuSR: Testing the Limits of Chain-of-thought with Multistep Soft Reasoningcited by 195
  9. 2024MoreHopQA: More Than Multi-hop Reasoningcited by 37
  10. 2023StrategyLLM: Large Language Models as Strategy Generators, Executors, Optimizers, and Evaluators for Problem Solvingcited by 13
  11. 2025LLMPC: Large Language Model Predictive Controlcited by 4
  12. 2025RISE: Reasoning Enhancement via Iterative Self-Exploration in Multi-hop Question Answeringcited by 3

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