This research paper introduces First-Explore Proximal Policy Optimization (FE-PPO), a new reinforcement learning algorithm designed to improve how agents discover rewards in complex, deceptive environments. While standard meta-learning methods often fail when immediate rewards are misleading, the FE-PPO framework trains agents specifically to gather information during exploration that will maximize success in later exploitation phases. By integrating a value function and bootstrapping into the original First-Explore objective, the authors significantly increase efficiency, achieving high performance with 10 to 40 times fewer samples. The study demonstrates that FE-PPO consistently outperforms the strong RL² baseline across various challenging benchmarks, including navigation tasks and bandit problems. Additionally, the authors provide a more competitive comparison by implementing a Transformer-XL architecture for their baselines. Ultimately, this work offers a practical, open-source foundation for future research into efficient meta-exploration strategies.