Bradley-Terry
bradley-terry-3b07d8c5·2 events·first seen 5d agoAliases: Bradley-Terry
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TuneJury: Open pairwise reward model for text-to-music preference alignment
Researchers introduce TuneJury, an open-source instance-level pairwise reward model for text-to-music generation that predicts preference scores from text prompts and audio clips. The model is trained on publicly available human-preference labels spanning arena votes, crowdsourced comparisons, and expert ratings. A post-hoc anchor calibration method enables efficient adaptation to new generators without full retraining. The reward model drives gains across best-of-N selection, latent optimization, and expert-iteration post-training.
OrchRM: Self-supervised reward modeling for multi-agent orchestration without human annotations
Researchers propose Orchestration Reward Modeling (OrchRM), a self-supervised framework that trains reward models for LLM-based multi-agent orchestrators using intermediate execution artifacts to construct win-lose pairs for Bradley-Terry training. The approach avoids costly sub-agent rollouts by operating directly at the orchestration level, achieving up to 10x improvement in training token efficiency and up to 8% accuracy gains in test-time scaling. Results generalize across mathematical reasoning, web-based QA, and multi-hop reasoning tasks.