# A13 — Init logit = 0.0 (maximum gradient, A starts at 0.5)
# Purpose: σ(0)=0.5, maximum gradient signal. Init NLL will be bad
#          but learning space is large. Tests if predictor can learn from scratch.
# Run: python scripts/train.py --config configs/a13_init_logit_0.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: 0.0

# 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: "a13-init-logit-0"
log_every: 10
eval_every: 500

# Checkpointing
save_every: 2500
save_dir: "checkpoints/a13/"

# Hardware
num_gpus: 1