How does LightGBM deal with value scale?
Generally, in tree-based models the scale of the features does not matter. This is because at each tree level, the score of a possible split will be equal whether the respective feature has been scaled or not.

You can think of it like here: We're dealing with a binary classification problem and the feature we're splitting takes values from 0 to 1000. If you split it on 300, the samples &lt;300 belong 90% to one category while those >300 belong 30% to one category. Now imaging this feature is scaled between 0 and 1. Again, if you split on 0.3, the sample &lt;0.3 belong 90% to one category while those >0.3 belong 30% to one category. 

So you've changed the splitting point but the actual distribution of the samples remains the same regarding the target variable.