Derailer-Rerailer: Adaptive Verification for Efficient and Reliable Language Model Reasoning
This addresses the problem of reliable and efficient reasoning for LLM users, offering a practical solution that balances performance and resource usage, though it is incremental in optimizing existing verification approaches.
The paper tackles the trade-off between reasoning accuracy and computational efficiency in large language models by proposing the Derailer-Rerailer framework, which adaptively verifies reasoning to achieve 8-11% accuracy improvements while maintaining 2-3 times better efficiency than existing methods.
Large Language Models (LLMs) have shown impressive reasoning capabilities, yet existing prompting methods face a critical trade-off: simple approaches often struggle with complex tasks and reasoning stability, while more sophisticated methods require multiple inferences and substantial computational resources, limiting their practical deployment. To address this challenge, we propose Derailer-Rerailer, a novel framework that adaptively balances reasoning accuracy and computational efficiency. At its core, our framework employs a lightweight Derailer mechanism to assess reasoning stability and selectively triggers an advanced Rerailer verification process only when necessary, thereby optimizing computational resource usage. Extensive evaluation across both open and closed-source models on more than 20 categories of mathematical, symbolic, and commonsense reasoning tasks demonstrates our framework's effectiveness: Derailer-Rerailer achieves significant accuracy improvements (8-11\% across various reasoning tasks) while maintaining 2-3 times better efficiency than existing verification methods, with particularly strong performance in mathematical and symbolic reasoning, offering a practical solution for enhancing LLM reasoning reliability while significantly reducing computational overhead.