Fix value function gradient: move to MLP device before backward pass

MLP lives on CPU, conditioning on GPU — device mismatch silently fell back
to fixed direction. Now moves conditioning to MLP device for the forward/
backward, then returns gradient to original device.

Also surface value function fallback errors instead of swallowing them.

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

value_function.py

@@ -73,11 +73,12 @@ class OnlineValueFunction(nn.Module):
 
     def gradient(self, conditioning):
         """∂reward/∂conditioning — same shape as input."""
+        mlp_device = next(self.parameters()).device
         with torch.inference_mode(False), torch.enable_grad():
-            c_in = conditioning.detach().clone().float().requires_grad_(True)
+            c_in = conditioning.detach().clone().float().to(mlp_device).requires_grad_(True)
             reward = self.forward(self.compress(c_in).unsqueeze(0))
             reward.backward()
-        return c_in.grad.to(conditioning.dtype)
+        return c_in.grad.to(conditioning.device, conditioning.dtype)
 
     def is_ready(self):
         return len(self.buffer_c) >= self.MIN_SAMPLES