Author

Wanyu Lin

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Preprint Jul 2026

Adopting Reinforcement Learning with Verifiable Rewards for Molecular Generation

This work proposes LLMol, a principled reinforcement learning framework that directly incorporates verifiable rewards for targeted molecule generation and introduces Reinforcement Learning with Verifiable Rewards (RLVR), which directly integrates property-based reward signals to guide molecular generation toward task-specific objectives.

Mingxuan Ouyang, Hao Lan, Wanyu Lin · 0 citations