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Generative reward models (GRMs) are important for LLM optimization. Unlike scalar reward models, GRMs generate natural-language critiques alongside preference judgments, providing finer-grained evaluation signals. Their effectiveness depends heavily on critique reliability. However, existing GRM training typically uses...
This work proposes LifeMem, a longitudinal memory framework that combines structured life-event retrieval with agent-specific parametric memory for experience integration, and shows that LifeMem improves alignment with human data in terms of response distributions, overall and within-group diversity, and patterns of wi...
This work introduces LongJudgeBench, a comprehensive benchmark for evaluating LLM judges on long-form outputs across diverse real-world scenarios and judging protocols, and systematically evaluates a broad range of LLM judges, covering multiple base models and judging settings.
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