You will be shown an excerpt from a computer science scientific research paper, followed by a question about the paper. Please answer the question. Do not include any text in your response other than the answer.

Context: The process of training a model’s parameters such that a few gradient steps, or even a single gradient step, can produce good results on a new task can be viewed from a feature learning standpoint as building an internal representation that is broadly suitable for many tasks. If the internal representation is suitable to many tasks, simply fine-tuning the parameters slightly (e.g. by primarily modifying the top layer weights in a feedforward model) can produce good results. In effect, our procedure optimizes for models that are easy and fast to fine-tune, allowing the adaptation to happen in the right space for fast learning. From a dynamical systems standpoint, our learning process can be viewed as maximizing the sensitivity of the loss functions of new tasks with respect to the parameters: when the sensitivity is high, small local changes to the parameters can lead to large improvements in the task loss. Our approach is also related to methods for initialization of deep networks. In computer vision, models pretrained on large-scale image classification have been shown to learn effective features for a range of problems (Donahue et al., 2014). In contrast, our method explicitly optimizes the model for fast adaptability, allowing it to adapt to new tasks with only a few examples.Our method can also be viewed as explicitly maximizing sensitivity of new task losses to the model parameters.A number of prior works have explored sensitivity in deep networks, often in the context of initialization (Saxe et al., 2014; Kirkpatrick et al., 2016). Most of these works have considered good random initializations, though a number of papers have addressed data-dependent initializers (Krähenbühl et al., 2016; Salimans & Kingma, 2016), including learned initializations (Husken & Goerick, 2000; Maclaurin et al., 2015). In contrast, our method explicitly trains the parameters for sensitivity on a given task distribution, allowing for extremely efficient adaptation for problems such as K-shot learning and rapid reinforcement learning in only one or a few gradient steps. In contrast to prior work, which has sought to train recurrent neural networks that ingest entire datasets (Santoro et al., 2016; Duan et al., 2016b) or feature embeddings that can be combined with nonparametric methods at test time (Vinyals et al., 2016; Koch, 2015), we propose a method that can learn the parameters of any standard model via meta-learning in such a way as to prepare that model for fast adaptation. The intuition behind this approach is that some internal representations are more transferrable than others. For example, a neural network might learn internal features that are broadly applicable to all tasks in p(\mathcal{T}), rather than a single individual task. How can we encourage the emergence of such general-purpose representations? We take an explicit approach to this problem: since the model will be fine-tuned using a gradient-based learning rule on a new task, we will aim to learn a model in such a way that this gradient-based learning rule can make rapid progress on new tasks drawn from p(\mathcal{T}), without overfitting. In effect, we will aim to find model parameters that are sensitive to changes in the task, such that small changes in the parameters will produce large improvements on the loss function of any task drawn from p(\mathcal{T}), when altered in the direction of the gradient of that loss (see Figure 1). We make no assumption on the form of the model, other than to assume that it is parametrized by some parameter vector \theta, and that the loss function is smooth enough in \theta that we can use gradient-based learning techniques.

Question: What type of parameter would be considered a 'good' initial parameter?
A good initial parameter is a parameter that gives good performance in many tasks even with a little fine-tuning of the parameter. This means that the loss function defined in many tasks is sensitive, and this sensitive loss leads to good updates.