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cs.AI2026
ADAPT for Science: A Multi-Agent System for Self-Evolving Scientific Optimization
Zhaoran Feng, Yuchen Ni, Jianan Liu +3
Scientific discovery often requires identifying a small number of high-performing designs in vast, constrained search spaces. This is difficult when evaluations are costly, feasibl…
cs.AI2026
PGPO: Potential-Guided Policy Optimization for Multi-Turn Agentic Tasks
Yuyao Zheng, Haipeng Sun, Junwei Bao +4
Group-based reinforcement learning (RL) has become an effective paradigm for LLM post-training, but in multi-turn agentic tasks with sparse terminal rewards, it often provides coar…