CRISP: Complex Reasoning with Interpretable Step-based Plans

• URL: http://arxiv.org/abs/2507.08037v1
• Date: Wed, 09 Jul 2025 11:40:24 GMT
• Title: CRISP: Complex Reasoning with Interpretable Step-based Plans
• Authors: Matan Vetzler, Koren Lazar, Guy Uziel, Eran Hirsch, Ateret Anaby-Tavor, Leshem Choshen,
• Abstract summary: We introduce CRISP (Complex Reasoning with Interpretable Step-based Plans), a dataset of high-level plans for mathematical reasoning and code generation.<n>We demonstrate that fine-tuning a small model on CRISP enables it to generate higher-quality plans than much larger models using few-shot prompting.
• Score: 15.656686375199921
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Recent advancements in large language models (LLMs) underscore the need for stronger reasoning capabilities to solve complex problems effectively. While Chain-of-Thought (CoT) reasoning has been a step forward, it remains insufficient for many domains. A promising alternative is explicit high-level plan generation, but existing approaches largely assume that LLMs can produce effective plans through few-shot prompting alone, without additional training. In this work, we challenge this assumption and introduce CRISP (Complex Reasoning with Interpretable Step-based Plans), a multi-domain dataset of high-level plans for mathematical reasoning and code generation. The plans in CRISP are automatically generated and rigorously validated--both intrinsically, using an LLM as a judge, and extrinsically, by evaluating their impact on downstream task performance. We demonstrate that fine-tuning a small model on CRISP enables it to generate higher-quality plans than much larger models using few-shot prompting, while significantly outperforming Chain-of-Thought reasoning. Furthermore, our out-of-domain evaluation reveals that fine-tuning on one domain improves plan generation in the other, highlighting the generalizability of learned planning capabilities.

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