Test-Time Personalization: A Diagnostic Framework and Probabilistic Fix for Scaling Failures

• URL: http://arxiv.org/abs/2605.10991v1
• Date: Sat, 09 May 2026 12:09:11 GMT
• Title: Test-Time Personalization: A Diagnostic Framework and Probabilistic Fix for Scaling Failures
• Abstract summary: We study scaling inference-time computation by sampling N candidates from a personalized policy model and selecting the best with a personalized reward model.<n>We prove that oracle selection yields expected utility growing logarithmically with the number of sampled candidates.<n>We propose a probabilistic personalized reward model whose learned variance effectively mitigates both failure modes.
• Score: 26.927205237105156
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Existing approaches to LLM personalization focus on constructing better personalized models or inputs, while treating inference as a single-shot process. In this work, we study Test-Time Personalization (TTP) along an unexplored axis: scaling inference-time computation by sampling N candidates from a personalized policy model and selecting the best with a personalized reward model. We prove that oracle selection yields expected utility growing logarithmically with the number of sampled candidates, establishing a theoretical ceiling for test-time scaling. However, standard reward models fail to realize this potential. To diagnose why, we derive a unified scaling law that decomposes any reward model's Best-of-N curve into four measurable quantities and reveals two failure modes, user-level collapse (near-constant prediction for some users) and query-level reward hacking (negative correlation with true quality for some queries). Guided by this law, we propose a probabilistic personalized reward model whose learned variance effectively mitigates both failure modes. Experiments confirm both elements of our framework: TTP delivers consistent scaling across multiple policy models and personalized text generation tasks, and our scaling law closely matches observed scaling curves across reward-model variants.

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