RMSE and R2 with different training splits I am running 2 linear regression models using the same data with different data splits. n=205
70/30 split
RMSE: 2341
R2: 0.85
50/50 split
RMSE: 2474
R2: 0.88
Seems counterintuitive that the R2 increases with the 50/50 split but the RMSE increases as well. What could this mean?

You are correct to notice that $R^2$ and $RMSE$ are both ways of expressing square loss, so it should raise concerns that they are not moving together.
However, $R^2$ also involves the square loss of a model that always naïvely guesses the pooled mean $\bar y$ every time, regardless of features.
A plausible explanation is that, while the $RMSE$ of your model increased, so did the $RMSE$ of the naïve model.
If you use the interpretation of $R^2$ that it is the proportion of variance explained (equivalent in certain settings), it is reasonable to think that you explain a greater proportion of the variance (model $RMSE$) if the total variance to be explained (naïve $RMSE$) is less, even though your $RMSE$ is greater.
$$
R^2=1-\dfrac{\sum\left(
y_i-\hat y_i
\right)^2}{\sum\left(
y_i-\bar y
\right)^2}
$$
