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Criterion-Distilled Policy Optimization (CriPO), which enhances rubric-based RL via on-policy self-distillation via on-policy self-distillation, to simultaneously address both unexplored and suppressed criteria.
Optimus is a general-purpose model transformation framework built in the PyTorch 2.x (PT2) machine learning compiler that applies an efficient greedy search algorithm for pattern matching and replacement, while preserving model semantic.
By merging chemical heuristics with physical laws, DrugRPG effectively addresses the hallucination crisis, shifting generative SBDD from scoring-oriented optimization toward high-fidelity, realistic lead discovery.
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