training: use rank 64, define as single constant

- DEFAULT_RANK = 64 in train_router.py
- All references use the constant, not magic numbers
- ~2.5GB optimizer state instead of ~10GB

Co-Authored-By: Proof of Concept <poc@bcachefs.org>

training/apollo_plugin/optimizer.py

@@ -8,9 +8,9 @@ Channel-wise or tensor-wise scaling is sufficient. Apollo approximates
 these scaling factors using a low-rank auxiliary optimizer state based on
 pure random projection.
 
-Default rank=256 (full Apollo). ~10GB state for 27B model, <0.25%
-compute overhead vs forward+backward. Captures gradient structure
-across 100+ behavioral training examples per batch.
+Default rank=64. ~2.5GB state for 27B model, <0.25% compute overhead
+vs forward+backward. Sufficient for behavioral training with diverse
+examples.
 
 Key implementation details from the paper:
   - Gradient scale factor α = √(n/r) compensates for projection ratio
@@ -34,7 +34,7 @@ class Apollo(Optimizer):
     Args:
         params: model parameters
         lr: learning rate (default: 1e-4)
-        rank: projection rank (default: 256)
+        rank: projection rank (default: 64)
         betas: Adam momentum coefficients (default: (0.9, 0.999))
         eps: numerical stability term (default: 1e-8)
         weight_decay: decoupled weight decay (default: 0.01)
@@ -46,7 +46,7 @@ class Apollo(Optimizer):
             Set to None to disable.
     """
 
-    def __init__(self, params, lr=1e-4, rank=256, betas=(0.9, 0.999),
+    def __init__(self, params, lr=1e-4, rank=64, betas=(0.9, 0.999),
                  eps=1e-8, weight_decay=0.01, warmup_steps=0,
                  scale=None, proj_refresh=200, norm_growth_limit=1.01):
         defaults = dict(lr=lr, rank=rank, betas=betas, eps=eps,