Add post_pos as a model target via travel_dir decomposition

step_length already encodes |post_pos - pre_pos| by definition, so a raw
post_pos target would duplicate that magnitude and could drift inconsistent
with step_length during sampling. Instead add travel_dir, a unit vector
(local frame) giving only the direction of pre_pos->post_pos; post_pos is
reconstructed at inference as pre_pos + step_length * travel_dir, keeping
the two self-consistent. Target grows from 6D to 9D.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

scripts/train.py

@@ -133,7 +133,7 @@ def main() -> None:
     print("fitting normalizer (streaming) …")
     events_arr = np.array(sorted(train_events))
     cond_acc = _WelfordAccumulator(9)
-    tgt_acc = _WelfordAccumulator(6)
+    tgt_acc = _WelfordAccumulator(9)
     for path in files:
         for chunk in iter_file_chunks(path):
             mask = np.isin(chunk["event_id"], events_arr)