chore(loop): 6 more iters — plateau at ~0.34-0.37 (1/6 kept)

Toy encoder near ceiling. 1 kept (val_vol_r2 0.3032→0.3442), 5 reverts.
Consistent plateau = time to swap in TS-JEPA backbone (#3/#5).

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

train.py

@@ -47,10 +47,10 @@ class Encoder(nn.Module):
         super().__init__()
         self.net = nn.Sequential(
             nn.Flatten(), 
-            nn.Linear(win * 2, 128), 
-            nn.LayerNorm(128),
+            nn.Linear(win * 2, 256), 
+            nn.LayerNorm(256),
             nn.GELU(), 
-            nn.Linear(128, emb)
+            nn.Linear(256, emb)
         )
 
     def forward(self, x):
@@ -61,7 +61,7 @@ def main():
     (Xtr, ytr), (Xte, yte) = build()
     Xtr_t = torch.tensor(Xtr, device=dev)
     enc = Encoder(WINDOW, EMBED_DIM).to(dev)
-    dec = nn.Sequential(nn.Linear(EMBED_DIM, 128), nn.GELU(), nn.Linear(128, WINDOW * 2)).to(dev)
+    dec = nn.Sequential(nn.Linear(EMBED_DIM, 256), nn.GELU(), nn.Linear(256, WINDOW * 2)).to(dev)
     opt = torch.optim.Adam(list(enc.parameters()) + list(dec.parameters()), lr=LR)
 
     for _ in range(EPOCHS):  # SSL: masked reconstruction of the window