Temper and Tilt Lead to SLOP: Reward Hacking Mitigation with Inference-Time Alignment
Inference-time alignment techniques offer a lightweight alternative or complement to costly reinforcement learning, while enabling continual adaptation as alignment objectives and reward targets evolve. Existing theoretical analyses justify these methods as approximations to sampling from distributions optimally tilted toward a given reward model. We extend these techniques by introducing reference-model temperature adjustment, which leads to further generalization of inference-time alignment to
Record details
Published: 13 May 2026
Source: arXiv
Category: Research
Topics: Safety & alignment
Retrieved: 14 July 2026
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ethics.ai (13 May 2026), “Temper and Tilt Lead to SLOP: Reward Hacking Mitigation with Inference-Time Alignment,” evidence record 4388, https://ethics.ai/record/4388 (originally published by arXiv).
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