Clarify MIPROv2 naming in docs to align with paper

docs/docs/deep-dive/optimizers/miprov2.md

@@ -217,4 +217,6 @@ These steps are broken down in more detail below:
 3. **Find an Optimized Combination of Few-Shot Examples & Instructions**. Finally, now that we've created these few-shot examples and instructions, we use Bayesian Optimization to choose which set of these would work best for each predictor in our program. This works by running a series of `num_trials` trials, where a new set of prompts are evaluated over our validation set at each trial. This helps the Bayesian Optimizer learn which combination of prompts work best over time. If `minibatch` is set to `True` (which it is by default), then the new set of prompts are only evaluated on a minibatch of size `minibatch_size` at each trial which generally allows for more efficient exploration / exploitation. The best averaging set of prompts is then evaluated on the full validation set every `minibatch_full_eval_steps` get a less noisey performance benchmark. At the end of the optimization process, the LM program with the set of prompts that performed best on the full validation set is returned.
 
 
-For those interested in more details, more information on `MIPROv2` along with a study on `MIPROv2` compared with other DSPy optimizers can be found in [this paper](https://arxiv.org/abs/2406.11695). 
+For those interested in more details, more information on `MIPROv2` along with a study on `MIPROv2` compared with other DSPy optimizers can be found in [this paper](https://arxiv.org/abs/2406.11695).
+
+**Note:** The paper refers to the algorithm as `MIPRO`, but it actually describes what is now called `MIPROv2`. The name `MIPROv2` was adopted in the repository to reflect the addition of features like mini-batching while ensuring backward compatibility with existing code built using the original `MIPRO`. This distinction might not be immediately clear in the paper, but all references to `MIPRO` in the paper correspond to `MIPROv2`.