Better Reward Models Are Not Always Less Biased: Social Alignment Beyond Standard Reward Benchmarks

Research demonstrates that superior performance on standard reward benchmarks does not guarantee reduced bias or balanced social alignment in large language models. An evaluation across bias, safety, morality, and ethical reasoning shows that conventional benchmarks mask critical behavioral differences, including the reinforcement of certain stereotypes despite the mitigation of others, as well as trade-offs between stereotype avoidance and contextual faithfulness.

Relying on standard AI benchmarks creates false confidence regarding model alignment and ethical behavior. This deficiency exposes organizations to unmonitored bias and compromised contextual judgment in deployed language systems. Addressing these blind spots is necessary to ensure reliable social alignment and systemic risk management.

High benchmark performance does not necessarily correlate with lower bias or improved social alignment in reward models.

Forward consideration, not a verified fact.