New methods improve agent skill evolution and reinforcement learning environments

Recent research introduces improved methods for evolving agent skills by ranking candidates with a language model, achieving 40% to 70% lower token costs. Work from NVIDIA, such as Skill2Env, further demonstrates compiling public agent skills into reinforcement learning environments.