SKATE, a Scalable Tournament Eval: Weaker LLMs differentiate between stronger ones using verifiable challenges

• URL: http://arxiv.org/abs/2508.06111v1
• Date: Fri, 08 Aug 2025 08:16:40 GMT
• Title: SKATE, a Scalable Tournament Eval: Weaker LLMs differentiate between stronger ones using verifiable challenges
• Authors: Dewi S. W. Gould, Bruno Mlodozeniec, Samuel F. Brown,
• Abstract summary: We introduce SKATE: a novel evaluation framework in which large language models (LLMs) compete by generating verifiable tasks for one another.<n>Our core is to treat evaluation as a game: models as both task-setters and solvers, incentivized to create questions which highlight their own strengths while exposing others' weaknesses.<n>Using a TrueSkill-based ranking system, we evaluate six LLMs and find that: (1) weaker models can reliably differentiate and score stronger ones, (2) LLM-based systems are capable of self-preferencing behavior, generating questions that align with their own capabilities, and (3) SKATE automatically surfaces fine
• Score: 2.184775414778289
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Evaluating the capabilities and risks of foundation models is paramount, yet current methods demand extensive domain expertise, hindering their scalability as these models rapidly evolve. We introduce SKATE: a novel evaluation framework in which large language models (LLMs) compete by generating and solving verifiable tasks for one another. Our core insight is to treat evaluation as a game: models act as both task-setters and solvers, incentivized to create questions which highlight their own strengths while exposing others' weaknesses. SKATE offers several key advantages, balancing scalability, open-endedness, and objectivity. It is fully automated, data-free, and scalable, requiring no human input or domain expertise. By using verifiable tasks rather than LLM judges, scoring is objective. Unlike domain-limited programmatically-generated benchmarks (e.g. chess-playing or spatial reasoning), having LLMs creatively pose challenges enables open-ended and scalable evaluation. As a proof of concept, we introduce LLM-set code-output-prediction (COP) challenges as a verifiable and extensible framework in which to test our approach. Using a TrueSkill-based ranking system, we evaluate six frontier LLMs and find that: (1) weaker models can reliably differentiate and score stronger ones, (2) LLM-based systems are capable of self-preferencing behavior, generating questions that align with their own capabilities, and (3) SKATE automatically surfaces fine-grained capability differences between models. Our findings are an important step towards general, scalable evaluation frameworks which can keep pace with LLM progress.

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