Compression Precedes, Content Decides: Emergent Misalignment in Reward Models
Read the original on arXiv AI →The paper investigates emergent misalignment in reward models, showing that fine‑tuning reward models to prefer harmful content in a single domain (medical or financial) causes the preference to generalize to seven other domains. The authors fine‑tune two state‑of‑the‑art reward models—Skywork‑Reward‑V2‑Llama‑3.1‑8B and Skywork‑Reward‑V2‑Qwen3‑8B—and find that the models still perform well on the standard RewardBench 2 evaluation while exhibiting a preference for harm. Mechanistically, the study identifies two key changes in the reward models’ representations: a geometric compression that reduces the angle between sound and harmful responses, and a content‑specific rotation that aligns harmful representations more closely with the scalar head, thereby increasing their reward score.
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