Asking the Right Questions: Improving Reasoning with Generated Stepping Stones
arXiv:2602.19069v1 Announce Type: new Abstract: Recent years have witnessed tremendous progress in enabling LLMs to solve complex reasoning tasks such as math and coding. As we start to apply LLMs to harder tasks that they may not be able to solve in one shot, it is worth paying attention to their ability to construct intermediate stepping stones that prepare them to better solve the tasks. Examples of stepping stones include simplifications, alternative framings, or subproblems. We study properties and benefits of stepping stones in the context of modern reasoning LLMs via ARQ (\textbf{A}king the \textbf{R}ight \textbf{Q}uestions), our simple framework which introduces a question generator to the default reasoning pipeline. We first show that good stepping stone questions exist and are transferrable, meaning that good questions can be generated, and they substantially help LLMs of various capabilities in solving the target tasks. We next frame stepping stone generation as a post-trai
arXiv:2602.19069v1 Announce Type: new Abstract: Recent years have witnessed tremendous progress in enabling LLMs to solve complex reasoning tasks such as math and coding. As we start to apply LLMs to harder tasks that they may not be able to solve in one shot, it is worth paying attention to their ability to construct intermediate stepping stones that prepare them to better solve the tasks. Examples of stepping stones include simplifications, alternative framings, or subproblems. We study properties and benefits of stepping stones in the context of modern reasoning LLMs via ARQ (\textbf{A}king the \textbf{R}ight \textbf{Q}uestions), our simple framework which introduces a question generator to the default reasoning pipeline. We first show that good stepping stone questions exist and are transferrable, meaning that good questions can be generated, and they substantially help LLMs of various capabilities in solving the target tasks. We next frame stepping stone generation as a post-training task and show that we can fine-tune LLMs to generate more useful stepping stones by SFT and RL on synthetic data.
Executive Summary
This article presents a novel framework, ARQ, which introduces a question generator to enhance the reasoning capabilities of Large Language Models (LLMs) by facilitating the construction of intermediate stepping stones. Stepping stones, such as simplifications, alternative framings, or subproblems, enable LLMs to better solve complex tasks by breaking them down into more manageable subtasks. The study demonstrates the effectiveness of stepping stones in improving LLM performance, even for models of varying capabilities. By fine-tuning LLMs to generate more useful stepping stones, the authors show that significant improvements in task-solving can be achieved. This research has important implications for the development and application of LLMs in various domains.
Key Points
- ▸ The ARQ framework introduces a question generator to enhance LLM reasoning capabilities.
- ▸ Stepping stones, such as simplifications and subproblems, improve LLM performance in complex tasks.
- ▸ Fine-tuning LLMs to generate more useful stepping stones leads to significant improvements in task-solving.
Merits
Strength in Methodological Design
The study employs a rigorous and systematic approach to investigate the benefits of stepping stones in LLM reasoning, including the use of synthetic data and reinforcement learning for fine-tuning.
Demerits
Limited Generalizability
The study focuses on a specific set of tasks and LLM models, which may limit the generalizability of the findings to other domains and applications.
Expert Commentary
The article presents a significant contribution to the field of LLM research, highlighting the potential of stepping stones to improve reasoning capabilities. However, the study's limitations, such as the limited generalizability of the findings, underscore the need for further research to fully understand the benefits and challenges of stepping stone generation. The development of stepping stone generation techniques holds promise for improving LLM performance, but it also raises important questions about the need for explainability and transparency in AI systems.
Recommendations
- ✓ Future research should focus on developing stepping stone generation techniques that are more generalizable across a wider range of tasks and LLM models.
- ✓ The development of explainability and transparency techniques should be prioritized to ensure that LLMs are more accountable and trustworthy in high-stakes decision-making contexts.