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BATON transforms long-horizon robot manipulation by making subtask exploration efficient and transition-aware, leading to significant performance gains.
Grounding preference learning in psychological and cultural constructs yields a significant boost in population alignment for LLMs, outperforming traditional demographic methods.
Quantization can increase bias in LLMs, with models generating stereotypes in one in four open-ended responses despite passing standard safety evaluations.
