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+# A12 — Init logit = 3.0 (moderate, sigmoid not saturated)
+# Purpose: A starts at σ(1.5)≈0.82, gradient ~250× larger than logit=15.
+#          Does predictor learn useful topology when sigmoid is not saturated?
+# Run: python scripts/train.py --config configs/a12_init_logit_3.yaml
+
+# Model
+olmo_model_id: "allenai/OLMo-2-0425-1B"
+qwen_model_id: "Qwen/Qwen3-Embedding-0.6B"
+
+# Predictor
+predictor_hidden_dim: 1024
+predictor_rank: 32
+cascading_gate_k: 5.0
+input_norm: "none"
+init_logit: 3.0
+
+# Data
+dataset: "allenai/dolma"
+dataset_name: "v1_7"
+seq_len: 1024
+batch_size: 4
+micro_batch_size: 2
+qwen_input_prefix: ""
+
+# Eval
+eval_skip: 10000
+eval_size: 50
+
+# Training — ~50M tokens = 12500 steps
+total_steps: 12500
+lr: 3e-4
+weight_decay: 0.01
+optimizer: "adamw"
+
+# Schedules — constant tau=2 (same as S2), no sparsity
+tau_init: 2.0
+tau_final: 2.0
+tau_schedule: "cosine"
+lambda_max: 0.0
+lambda_warmup_frac: 0.2
+
+# Logging
+wandb_project: "dagformer"
+wandb_run_name: "a12-init-logit-3"
+log_every: 10
+eval_every: 500
+
+# Checkpointing
+save_every: 2500
+save_dir: "checkpoints/a12/"
+
+# Hardware
+num_gpus: 1