Imagine you are training a large language model to solve complex, multi-step mathematical word problems. You are considering two different strategies for providing feedback to the model during its training:

• Strategy 1: The model generates a complete solution, and a reward is given only based on whether the final numerical answer is correct.
• Strategy 2: The model generates a solution step-by-step, and a reward is given after each step based on the logical correctness of that specific step.

Analyze the trade-offs between these two strategies. Discuss the potential impact of each strategy on the model's final reasoning ability, the risk of the model learning flawed problem-solving methods, and the practical challenges of implementing each feedback system.