# Diverge to Induce Prompting: Multi-Rationale Induction for Zero-Shot Reasoning

arXiv: https://arxiv.org/abs/2602.08028 · HTML: https://mlsanity.com/paper/2602.08028/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Prompting and chain-of-thought reasoning, one of 17 directions in LLM reasoning.
- Builds on 0 of its 38 references that are in the map.
- First posted on arXiv 8 Feb 2026.

## Placed in

- LLM reasoning / [Prompting and chain-of-thought reasoning](https://mlsanity.com/lr/prompting-and-chain-of-thought-reasoning/)

## Built on by (most cited first, 2 of 2)

- [Strategy-Induct: Task-Level Strategy Induction for Instruction Generation](https://mlsanity.com/paper/2605.20924/) (2026)
- [Smarter by the Moment: Environment-Driven Dynamic Policies for Continual LLM Improvement](https://mlsanity.com/paper/2609.16800/) (2026)

## Abstract (arXiv)

To address the instability of unguided reasoning paths in standard Chain-of-Thought prompting, recent methods guide large language models (LLMs) by first eliciting a single reasoning strategy. However, relying on just one strategy for each question can still limit performance across diverse tasks. We propose Diverge-to-Induce Prompting (DIP), a framework that first prompts an LLM to generate multiple diverse high-level rationales for each question. Each rationale is then elaborated into a detailed, step-by-step draft plan. Finally, these draft plans are induced into a final plan. DIP enhances zero-shot reasoning accuracy without reliance on resource-intensive sampling. Experiments show that DIP outperforms single-strategy prompting, demonstrating the effectiveness of multi-plan induction for prompt-based reasoning.