arXiv Machine Learning
MARCO: Multi-Round Agentic Reinforcement for Conditional Molecular Optimization
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On the Design Space of Discrete Diffusion Online Adaptation for Molecular Optimization
arXiv:2607. 02834v1 Announce Type: new Abstract: Molecular optimization often starts from a pretrained generative model that captures a broad prior over valid molecular structures.
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Q-Steer: Action-Value Guidance for Molecular Policy Optimization
Oracle-limited molecular optimization gives reward only after a complete molecule is generated, while each rollout requires many local next-token decisions. This delayed-feedback interface makes molecular policy optimization myopic: an optimizer can learn that a molecule was good without knowing which intermediate actions made it good.
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Fine-Tuning Diffusion Models for Molecular Generation via Reinforcement Learning and Fast Sampling
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