arXiv:2511.21734v2 Announce Type: replace 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 complementary to standard forward Chain-of-Thought (CoT), which restricts the logical search space of the answer by pruning the LLM's output distribution. We further generalize VF prompting t
The continuous drive to enhance LLM capabilities without incurring significant training costs or extensive sampling leads to the development of novel prompting strategies like Verification-First.
This development offers a method to improve LLM reasoning and efficiency, potentially accelerating the development and deployment of more robust AI applications and agents.
The paradigm for optimizing LLM performance may shift towards 'reverse reasoning' verification strategies, making AI more accessible and performant with fewer resources.
- · AI developers
- · Companies deploying LLMs
- · Cloud AI providers
- · AI researchers
- · Companies heavily invested in compute-intensive LLM fine-tuning
- · Developers relying solely on brute-force scaling for LLM improvement
LLMs become more reliable and efficient at complex reasoning tasks, reducing inference costs.
The improved efficiency accelerates the deployment of sophisticated AI agents across various industries, creating new automation opportunities.
Accessibility to high-performing AI expands, democratizing advanced AI development and fostering a more diverse ecosystem of AI applications.
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