An engineering team is training a robot arm using a policy gradient method, where the robot learns by trial and error. Collecting new data by running the robot is time-consuming and expensive. Two different training strategies are proposed:

Strategy A: After every 1,000 trials, the robot's decision-making model (policy) is updated. All data from the previous 1,000 trials is then discarded, and a new set of 1,000 trials is collected using the updated model.

Strategy B: The policy is also updated after every 1,000 trials. However, to be more data-efficient, the data from old trials is kept and reused for multiple subsequent updates. To do this, the value of each past action is adjusted by multiplying it by a ratio: (the probability of taking that action under the current policy) / (the probability of taking that action under the policy that originally generated the data).

Critically evaluate the two strategies. Which strategy is likely to be more effective for training the robot arm, and why? Justify your answer by explaining the trade-offs between the two approaches regarding data efficiency and potential training instability.