Mitigating Legibility Tax with Decoupled Prover-Verifier Games
#Large language models #Legibility tax #Prover-verifier games #Model checkability #AI accuracy #Decoupled training #Translator models
📌 Key Takeaways
- Researchers propose a solution to mitigate legibility tax in large language models
- The approach decouples correctness from checkability conditions
- A 'translator' model converts solver outputs into checkable forms
- The decoupled prover-verifier game creates equilibria for faithful translators
📖 Full Retelling
Researchers Yegon Kim and Juho Lee published a paper on February 26, 2026, proposing a novel approach to address the 'legibility tax' problem in large language models, where models trained for checkability suffer from reduced accuracy compared to those trained only for correctness. The paper introduces a solution by decoupling the correctness from checkability conditions, introducing a 'translator' model that converts a solver model's solution into a checkable form while maintaining the original answer's accuracy. This innovative approach allows researchers to first train a solver model to maximize correctness, and then separately train a translator to make the solver's outputs more checkable without compromising their accuracy. The researchers formulated a decoupled prover-verifier game where the equilibria correspond to faithful and checkable translators, creating a framework that balances both accuracy and verifiability in large language models. As AI systems become increasingly sophisticated, the ability to verify their outputs using less capable systems becomes crucial for safety, reliability, and practical deployment in various applications.
🏷️ Themes
Artificial Intelligence, Model Verification, Computational Efficiency
📚 Related People & Topics
Large language model
Type of machine learning model
A large language model (LLM) is a language model trained with self-supervised machine learning on a vast amount of text, designed for natural language processing tasks, especially language generation. The largest and most capable LLMs are generative pre-trained transformers (GPTs) that provide the c...
Entity Intersection Graph
Connections for Large language model:
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Educational technology
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Reinforcement learning
3 shared
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Machine learning
2 shared
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Artificial intelligence
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Benchmark
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Original Source
--> Computer Science > Artificial Intelligence arXiv:2602.23248 [Submitted on 26 Feb 2026] Title: Mitigating Legibility Tax with Decoupled Prover-Verifier Games Authors: Yegon Kim , Juho Lee View a PDF of the paper titled Mitigating Legibility Tax with Decoupled Prover-Verifier Games, by Yegon Kim and 1 other authors View PDF HTML Abstract: As large language models become increasingly capable, it is critical that their outputs can be easily checked by less capable systems. Prover-verifier games can be used to improve checkability of model outputs, but display a degradation in accuracy compared to a baseline trained only to maximize correctness -- a phenonemon named legibility tax. We propose a solution by decoupling the correctness from the checkability condition and instead training a "translator" model that turns a fixed solver model's solution into a checkable form. This allows us to first train the solver to maximize correctness, and then train the translator to translate the solver into a checkable form while retaining the solver's answer. To accommodate this new objective of translation, we formulate a decoupled prover-verifier game where the equilibria correspond to faithful and checkable translators. Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2602.23248 [cs.AI] (or arXiv:2602.23248v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2602.23248 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Yegon Kim [ view email ] [v1] Thu, 26 Feb 2026 17:25:22 UTC (142 KB) Full-text links: Access Paper: View a PDF of the paper titled Mitigating Legibility Tax with Decoupled Prover-Verifier Games, by Yegon Kim and 1 other authors View PDF HTML TeX Source view license Current browse context: cs.AI < prev | next > new | recent | 2026-02 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provid...
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