RankJudge creates paired multi-turn conversations with isolated single-turn flaws to generate unambiguous benchmarks for LLM-as-a-judge systems across ML, biomedicine, and finance domains.
Training language models to follow instructions with human feedback
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RAC adds ranking-aware group loss and clean-corrupted pairwise loss to RL post-training to boost both accuracy and calibration in multimodal reasoning without extra annotations.
citing papers explorer
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RankJudge: A Multi-Turn LLM-as-a-Judge Synthetic Benchmark Generator
RankJudge creates paired multi-turn conversations with isolated single-turn flaws to generate unambiguous benchmarks for LLM-as-a-judge systems across ML, biomedicine, and finance domains.
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Ranking-Aware Calibration for Reliable Multimodal Reinforcement Learning
RAC adds ranking-aware group loss and clean-corrupted pairwise loss to RL post-training to boost both accuracy and calibration in multimodal reasoning without extra annotations.