Spectral Souping learns offline specialized policies for fine-grained preferences and merges them online using a discovered universal spectral representation for efficient LLM alignment.
Modular pluralism: Pluralistic alignment via multi-llm collaboration
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 3verdicts
UNVERDICTED 3representative citing papers
Personalized RewardBench reveals that state-of-the-art reward models reach only 75.94% accuracy on personalized preferences and shows stronger correlation with downstream BoN and PPO performance than prior benchmarks.
Pluralistic AI alignment requires surfacing value conflicts via scoping, signalling, and repair rather than preference aggregation alone, as evidenced by low repair quality on contested prompts in tested frontier models.
citing papers explorer
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Personalized RewardBench: Evaluating Reward Models with Human Aligned Personalization
Personalized RewardBench reveals that state-of-the-art reward models reach only 75.94% accuracy on personalized preferences and shows stronger correlation with downstream BoN and PPO performance than prior benchmarks.