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cs.AI2026
ReSkill: Reconciling Skill Creation with Policy Optimization in Agentic RL
Zelin He, Haotian Lin, Boran Han +6
Agentic reinforcement learning (RL) enables LLM agents to improve continuously from environment rewards, yet the resulting policies do not systematically accumulate reusable strate…
cs.LG2026★ 1 cited
TabPrep: Closing the Feature Engineering Gap in Tabular Benchmarks
Andrej Tschalzev, Nick Erickson, Yuyang Wang +4
Progress in tabular machine learning has largely focused on increasingly sophisticated model architectures. At the same time, feature engineering remains a critical yet underexplor…