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The results show that procedural fairness failures in alignment can arise from structural design choices in reward learning, even in controlled, noise-free settings, with direct implications for large language models and agentic systems, where biased reward models can compound inequities across sequential decisions.
This work examines emoji-augmented prompts as a test case for robustness, evaluating 50 prompts across four open-source LLMs, showing substantial variation in robustness.
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