Imagine training a massive language model like teaching a dog a complex trick. Showing it once might suffice for a simple task, but mastering a complicated routine takes countless repetitions. This analogy mirrors a crucial challenge in training large language models (LLMs): finding the right "learning rate" as the amount of training data increases. Much like adjusting a dog treat's size based on the complexity of the trick, LLMs require smaller learning rate adjustments as they're exposed to more data. This research delves into the fascinating interplay between the learning rate and the amount of training data, termed the "token horizon." Through extensive experimentation, researchers demonstrate that longer training horizons necessitate smaller learning rates, similar to how a dog requires smaller treats after many repetitions. Furthermore, they've uncovered a 'scaling law' that allows precise prediction of the optimal learning rate for longer training based on the results from shorter, manageable experiments. This is akin to predicting the ideal treat size for advanced tricks based on the dog’s behavior during early training. The research reveals that some state-of-the-art models like LLama-1 may have used a learning rate that was too large, emphasizing the significance of data scaling on performance. This discovery has implications for optimizing LLM training, leading to faster convergence and improved performance. By understanding and implementing these scaling laws, we can unlock the true potential of LLMs, making them more efficient learners.