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We release ATLaS and EEF that study better usage of expert trajectories in supervised/reinforcement finetuning of LLM agents. ATLaS selects only 30% critical steps from expert trajectories to achieve better generalization across tasks/envs, while EEF show that expert failed trajectories can considerably improve the agent exploration in RL.