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cs.LG2026
Quantifying the Effect of Test Set Contamination on Generative Evaluations
Rylan Schaeffer, Joshua Kazdan, Baber Abbasi +8
As frontier AI systems are pretrained on web-scale data, test set contamination has become a critical concern for accurately assessing their capabilities. While research has thorou…
cs.LG2024
Scalable Ensembling For Mitigating Reward Overoptimisation
Ahmed M. Ahmed, Rafael Rafailov, Stepan Sharkov +2
Reinforcement Learning from Human Feedback (RLHF) has enabled significant advancements within language modeling for powerful, instruction-following models. However, the alignment o…