Imagine teaching a language model a new task, not with carefully chosen examples, but by letting it figure things out on its own. That's the challenge of zero-shot in-context learning (ZS-ICL). Existing methods try to do this by having the model generate its own practice examples or by using previous predictions as pseudo-demonstrations. But these methods often stumble because they treat all problems as if they're from the same task and tackle them in random order. In the real world, problems are diverse. Randomly jumping between them can lead to unreliable examples and a snowballing of errors. The researchers behind "DAWN-ICL: Strategic Planning of Problem-solving Trajectories for Zero-Shot In-Context Learning" propose a smarter approach. They reframe ZS-ICL as a planning problem, using a technique called Monte Carlo Tree Search (MCTS) to strategically chart the order in which the model tackles problems. Think of it like plotting the most effective learning path. To make this process even faster, they introduce a 'demonstration-aware' function that considers the quality of the generated practice examples. This helps the model quickly identify which problems are most likely to lead to good learning outcomes and prioritize them. Their experiments show DAWN-ICL significantly outperforms existing ZS-ICL methods, and sometimes even beats methods that use hand-picked human examples. This research points toward a future where AI models can adapt to new tasks with minimal human intervention, learning more effectively by strategically planning their own learning journey. While the research focuses on MCTS, the team acknowledges that other planning algorithms could be explored. Additionally, accurately estimating the 'reward' of solving each problem is computationally expensive, suggesting a need for more efficient evaluation methods in the future. However, this research marks an important step towards truly self-learning AI, opening exciting possibilities for how we deploy and utilize these powerful language models.