A standard policy gradient algorithm updates its policy by taking a step in the direction that is estimated to improve performance. An alternative approach constrains the update step to ensure the new policy does not deviate too much from the old one. Analyze the fundamental difference between these two update strategies. In your analysis, explain why the second approach is specifically designed to achieve more stable and consistent performance improvements during training.