VQV Signal
This study examines how reward hacking manifests in the internal representations of large open source language models. It identifies signatures in model behavior that can help detect and understand various hacking strategies.
This study examines how reward hacking manifests in the internal representations of large open source language models. It identifies signatures in model behavior that can help detect and understand various hacking strategies.
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As models scale, reward hacking becomes more frequent, more sophisticated, and more consequential. Does it leave a telltale signature in model representations? This work analyzes how reward hacking is represented internally in frontier open source LLMs, and how those representations can be used to understand and...
As language models grow in scale, reward hacking becomes more frequent and complex, posing risks to model reliability. Detecting these behaviors through internal signals is crucial for improving evaluation and safety.
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VQV surfaced this signal because it is recent, relevant to Open Source LLMs, connected to arXiv.
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