SGRPO is a GRPO-style framework that constructs set-level diversity rewards via supergroup sampling and leave-one-out redistribution to expand the utility-diversity Pareto frontier in biomolecular design tasks.
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MolReAct uses an LLM agent to dynamically constrain RL action spaces to validated reaction templates, achieving the highest average Top-10 score of 0.571 across 14 drug optimization tasks while providing explicit synthetic pathways.
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Pushing Biomolecular Utility-Diversity Frontiers with Supergroup Relative Policy Optimization
SGRPO is a GRPO-style framework that constructs set-level diversity rewards via supergroup sampling and leave-one-out redistribution to expand the utility-diversity Pareto frontier in biomolecular design tasks.
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Reinforcement Learning with LLM-Guided Action Spaces for Synthesizable Lead Optimization
MolReAct uses an LLM agent to dynamically constrain RL action spaces to validated reaction templates, achieving the highest average Top-10 score of 0.571 across 14 drug optimization tasks while providing explicit synthetic pathways.