# Strategic Chain-of-Thought: Guiding Accurate Reasoning in LLMs through Strategy Elicitation

arXiv: https://arxiv.org/abs/2409.03271 · HTML: https://mlsanity.com/paper/2409.03271/

## Key facts

- Cited by 19 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 23 of its 31 references that are in the map.
- First posted on arXiv 5 Sep 2024.

## Placed in

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

## Builds on (most cited first, 23 of 23)

- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Training Verifiers to Solve Math Word Problems](https://mlsanity.com/paper/2110.14168/) (2021)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Measuring Massive Multitask Language Understanding](https://mlsanity.com/paper/2009.03300/) (2020)
- [Self-Consistency Improves Chain of Thought Reasoning in Language Models](https://mlsanity.com/paper/2203.11171/) (2022)
- [Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge](https://mlsanity.com/paper/1803.05457/) (2018)
- [Mistral 7B](https://mlsanity.com/paper/2310.06825/) (2023)
- [Qwen2 Technical Report](https://mlsanity.com/paper/2407.10671/) (2024)
- [ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools](https://mlsanity.com/paper/2406.12793/) (2024)
- [Multimodal Chain-of-Thought Reasoning in Language Models](https://mlsanity.com/paper/2302.00923/) (2023)
- [Did Aristotle Use a Laptop? A Question Answering Benchmark with Implicit Reasoning Strategies](https://mlsanity.com/paper/2101.02235/) (2021)
- [Automatic Chain of Thought Prompting in Large Language Models](https://mlsanity.com/paper/2210.03493/) (2022)
- [MathQA: Towards Interpretable Math Word Problem Solving with Operation-Based Formalisms](https://mlsanity.com/paper/1905.13319/) (2019)
- [Program Induction by Rationale Generation : Learning to Solve and Explain Algebraic Word Problems](https://mlsanity.com/paper/1705.04146/) (2017)
- [Towards Reasoning in Large Language Models: A Survey](https://mlsanity.com/paper/2212.10403/) (2022)
- [Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models](https://mlsanity.com/paper/2305.04091/) (2023)
- [Chain-of-Thought Reasoning Without Prompting](https://mlsanity.com/paper/2402.10200/) (2024)
- [Take a Step Back: Evoking Reasoning via Abstraction in Large Language Models](https://mlsanity.com/paper/2310.06117/) (2023)
- [Meta-Prompting: Enhancing Language Models with Task-Agnostic Scaffolding](https://mlsanity.com/paper/2401.12954/) (2024)
- [Buffer of Thoughts: Thought-Augmented Reasoning with Large Language Models](https://mlsanity.com/paper/2406.04271/) (2024)
- [An Empirical Evaluation of Prompting Strategies for Large Language Models in Zero-Shot Clinical Natural Language Processing](https://mlsanity.com/paper/2309.08008/) (2023)
- [Re-TASK: Revisiting LLM Tasks from Capability, Skill, and Knowledge Perspectives](https://mlsanity.com/paper/2408.06904/) (2024)

## Built on by (most cited first, 19 of 19)

- [From System 1 to System 2: A Survey of Reasoning Large Language Models](https://mlsanity.com/paper/2502.17419/) (2025)
- [Generative to Agentic AI: Survey, Conceptualization, and Challenges](https://mlsanity.com/paper/2504.18875/) (2025)
- [MCTS-Judge: Test-Time Scaling in LLM-as-a-Judge for Code Correctness Evaluation](https://mlsanity.com/paper/2502.12468/) (2025)
- [Adversarial Reasoning at Jailbreaking Time](https://mlsanity.com/paper/2502.01633/) (2025)
- [GR2: Generative Reasoning Re-ranker](https://mlsanity.com/paper/2602.07774/) (2026)
- [A Multi-Agent Approach to Fault Localization via Graph-Based Retrieval and Reflexion](https://mlsanity.com/paper/2409.13642/) (2024)
- [The wall confronting large language models](https://mlsanity.com/paper/2507.19703/) (2025)
- [URSA: The Universal Research and Scientific Agent](https://mlsanity.com/paper/2506.22653/) (2025)
- [Verbosity-Aware Rationale Reduction: Effective Reduction of Redundant Rationale via Principled Criteria](https://mlsanity.com/paper/2412.21006/) (2024)
- [Before you <think>, monitor: Implementing Flavell's metacognitive framework in LLMs](https://mlsanity.com/paper/2510.16374/) (2025)
- [Efficient Thought Space Exploration Through Strategic Intervention](https://mlsanity.com/paper/2511.10038/) (2025)
- [No One Fits All: From Fixed Prompting to Learned Routing in Multilingual LLMs](https://mlsanity.com/paper/2604.16937/) (2026)
- [Assessing mentalization in humans and large language models](https://mlsanity.com/paper/2608.26291/) (2026)
- [SCoTT: Strategic Chain-of-Thought Tasking for Wireless-Aware Robot Navigation in Digital Twins](https://mlsanity.com/paper/2411.18212/) (2024)
- [D2S-FLOW: Automated Parameter Extraction from Datasheets for SPICE Model Generation Using Large Language Models](https://mlsanity.com/paper/2502.16540/) (2025)
- [Keyword-Centric Prompting for One-Shot Event Detection with Self-Generated Rationale Enhancements](https://mlsanity.com/paper/2508.07598/) (2025)
- [Human-AI collaboration or obedient and often clueless AI in instruct, serve, repeat dynamics?](https://mlsanity.com/paper/2508.10919/) (2025)
- [Strategy-Induct: Task-Level Strategy Induction for Instruction Generation](https://mlsanity.com/paper/2605.20924/) (2026)
- [GR2 Technical Report](https://mlsanity.com/paper/2606.31984/) (2026)

## Abstract (arXiv)

The Chain-of-Thought (CoT) paradigm has emerged as a critical approach for enhancing the reasoning capabilities of large language models (LLMs). However, despite their widespread adoption and success, CoT methods often exhibit instability due to their inability to consistently ensure the quality of generated reasoning paths, leading to sub-optimal reasoning performance. To address this challenge, we propose the Strategic Chain-of-Thought (SCoT), a novel methodology designed to refine LLM performance by integrating strategic knowledge prior to generating intermediate reasoning steps. SCoT employs a two-stage approach within a single prompt: first eliciting an effective problem-solving strategy, which is then used to guide the generation of high-quality CoT paths and final answers. Our experiments across eight challenging reasoning datasets demonstrate significant improvements, including a 21.05% increase on the GSM8K dataset and 24.13% on the TrackingObjects dataset, respectively, using the Llama3-8b model. Additionally, we extend the SCoT framework to develop a few-shot method with automatically matched demonstrations, yielding even stronger results. These findings underscore the efficacy of SCoT, highlighting its potential to substantially enhance LLM performance in complex reasoning tasks.