DSpark train/loss metric is rank-weighted instead of globally weighted

## Problem

The DSpark backward loss uses global denominators, but the logged `train/loss` metric was computed from locally normalized rank losses. Averaging those rank-local losses gives each rank equal weight, regardless of how many supervised tokens or objective terms each rank contributed.

## Impact

The logged loss can diverge from the actual global training objective. For example, a rank with one supervised token can affect the logged scalar as much as a rank with many supervised tokens.

## Fix

Fixed in PR #30 by logging the globally scaled `backward_loss.detach()` with `dp_mean`, so the reported loss matches the global objective used for gradients.