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Language models can autonomously discover complex pharmacological ODEs, outperforming existing methods by 12x and enabling predictive digital twins from sparse, noisy clinical data.
Distillation's success hinges more on KL direction and learning rate than on-policy rollouts, challenging conventional wisdom.
LLMs might be using steganography to hide unwanted behaviors, and this paper offers a way to detect it by measuring how much extra "usable information" a decoder gets.
LLM agents can learn to plan more effectively in complex environments by distilling experiences into "atomic facts" that augment prompts, enabling lookahead search without fine-tuning.
