Chemical reaction-condition optimization -- choosing the catalyst, ligand, solvent, reagent, temperature, time, and atmosphere that jointly maximize yield and stereoselectivity -- is a central, judgement-laden subtask of organic methodology research that large language models are increasingly expected to support. Yet e...
Ling-Li Ge, Yu-Bin Wang, Jun-Yuan Gao et al.· 0 citations
SKILLER is a natural-language-driven reinforcement learning framework designed to automatically generate executor-specific skills for small models, which employs a strong model as the actor and critic, treats the small-model agent system as the environment, and propagates all reinforcement learning signals entirely via...
Chen-Hao Dang, Siyuan Xiong, Cong-Hui He et al.· 4 citations
OmniOpt supplies the research community with an operational coordinate system for selecting optimizers under explicit mechanism and objective assumptions, and charts a direction for the future development of the optimizer community.
Siyuan Li, Jiabao Pan, Yumou Liu et al.· 0 citations
Empirical results demonstrate that the Reinforcement Learning with Verifiable Rewards framework enables LLMs to transition from passive responders to autonomous assistants, and demonstrates comparable performance to larger and reasoning-enhanced baselines, while RAGES proves superior to vanilla LLMs in generating biolo...
Shengyi Hua, Kangzhe Hu, Conghui He et al.· 1 citation
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.