Asking LLMs to Verify First is Almost Free Lunch

TL;DR

The paper introduces Verification-First (VF), a cost-effective prompting strategy for LLMs that improves reasoning accuracy by verifying answers before solution generation, outperforming traditional methods across multiple tasks.

Contribution

It proposes VF and Iter-VF strategies that leverage reverse reasoning to enhance LLM reasoning without significant additional costs.

Findings

VF with random answers outperforms standard CoT

Iter-VF surpasses existing test-time scaling methods

Method is effective across diverse benchmarks and models

Abstract

To enhance the reasoning capabilities of Large Language Models (LLMs) without high costs of training, nor extensive test-time sampling, we introduce Verification-First (VF), a strategy that prompts models to verify a provided candidate answer, even a trivial or random one, before generating a solution. This approach triggers a "reverse reasoning" process that is cognitively easier and complementary to standard forward Chain-of-Thought (CoT), effectively invoking the model's critical thinking to reduce logical errors. We further generalize the VF strategy to Iter-VF, a sequential test-time scaling (TTS) method that iteratively cycles the verification-generation process using the model's previous answer. Extensive experiments across various benchmarks (from mathematical reasoning to coding and agentic tasks) and various LLMs (from open-source 1B to cutting-edge commercial ones) confirm that VF with random answer consistently outperforms standard CoT with minimal computational overhead, and Iter-VF outperforms existing TTS strategies.