New Study: Cross-Family LLM Verification Significantly Boosts Accuracy for Math and Logic Problems

New research from the Agentic Learning AI Lab at New York University reveals that the effectiveness of large language model (LLM) verification is highly dependent on the verifier's model family and the nature of the task. Published on arXiv on December 2, 2025, the study, titled "When Does Verification Pay Off? A Closer Look at LLMs as Solution Verifiers," demonstrates that using an LLM from a different model family for verification yields substantially better results, particularly for structured mathematical and logical problems.

The comprehensive study, conducted by Jack Lu, Ryan Teehan, Jinran Jin, and Mengye Ren, evaluated 37 models across nine benchmarks, including logical reasoning, structured puzzles, mathematics, and factual recall. Researchers introduced "verifier gain" as a metric to quantify performance improvements from verifier-based rejection sampling, comparing three scenarios: a model checking itself (self-verification), models checking siblings within the same family (intra-family verification), and models checking other families (cross-family verification).

"Using 1 LLM to check another really pays off only when the verifier is a different model family and the task is structured math or logic," Rohan Paul stated in a tweet summarizing the findings. Self-checking showed very small verifier gain, as models tend to trust reasoning that aligns with their own, leading to the acceptance of similar incorrect answers.

The research further indicates that post-trained instruction models become strong verifiers for other model families, even if they show poor self-improvement. This suggests that while post-training enhances a model's problem-solving ability, it can also sharpen its bias in self-verification, making cross-family verification even more crucial for robust outcomes.

Paul added, "Across datasets, mathematical and logical problems are easier to verify than fact heavy questions, so verifier based resampling helps most on those structured tasks." The study found that tasks requiring precise logical reasoning or symbolic computation, such as 3-SAT and Sudoku, exhibit higher inherent verifiability compared to those relying on extensive factual recall or commonsense knowledge.

These findings highlight a critical insight for developing more reliable AI systems: diversifying the verification process with models from different architectural families can significantly mitigate biases and improve accuracy, especially for complex, structured problem-solving. The paper underscores the importance of strategic verifier selection in the evolving landscape of LLM applications.