An engineer is training a language model using a reinforcement learning process. They observe that the model's value function, which is supposed to predict the expected future reward from a given state, is consistently overestimating. During a single training step, the value function's output for the current state is 5.0. The calculated target value, based on the immediate reward and the next state's value, is 3.5. The training objective is to minimize the squared error between the prediction and the target: (target - prediction)^2. To achieve this objective, how should the training algorithm adjust the value function's output for the current state, and why is this adjustment necessary?