cascade root cause found: tied readout + default N(0,1) embedding init = pathological top-CE stiffness once predictions sharpen (sigma_tok~76 vs GPT-standard 0.02 giving ~1.6); add --untie and --tok_init; diag arms A/B/C

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_014FAPDWQ49M5Ye3NpTndTpn

1 files changed, 4 insertions, 0 deletions

diff --git a/ep_run/casc_bp_train.py b/ep_run/casc_bp_train.py
index 8e039ec..5194c45 100644
--- a/ep_run/casc_bp_train.py
+++ b/ep_run/casc_bp_train.py
@@ -14,6 +14,7 @@ ap.add_argument('--lr', type=float, default=3e-4); ap.add_argument('--warmup', t
 ap.add_argument('--seed', type=int, default=0)
 ap.add_argument('--save_every', type=int, default=500); ap.add_argument('--log', type=int, default=200)
 ap.add_argument('--wandb', default=''); ap.add_argument('--wandb_run', default='')
+ap.add_argument('--tok_init', type=float, default=0.0)  # >0: init tok/pos std (GPT-standard 0.02)
 args = ap.parse_args()
 torch.manual_seed(args.seed)
 dev = 'cuda' if torch.cuda.is_available() else 'cpu'
@@ -40,6 +41,9 @@ class Block(nn.Module):
 
 tok = nn.Embedding(vocab, args.C).to(dev)
 pos = nn.Embedding(args.T, args.C).to(dev)
+if args.tok_init > 0:
+    with torch.no_grad():
+        tok.weight.normal_(0, args.tok_init); pos.weight.normal_(0, args.tok_init)
 blocks = nn.ModuleList([Block(args.C, args.H) for _ in range(args.L)]).to(dev)
 mask = torch.triu(torch.full((args.T, args.T), float('-inf'), device=dev), 1)
 params = list(tok.parameters()) + list(pos.parameters()) + list(blocks.parameters())