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name: srm.srm_aol_v1@StableRecursionModel_ACTV1
loss:
name: losses@ACTLossHead
loss_type: stablemax_cross_entropy
halt_exploration_prob: 0.1
halt_max_steps: 16
# SRM-specific
n_iters: 12 # joint micro-steps per ACT step (≈ HRM's H_cycles·L_cycles+H_cycles = 6 with deeper schedule)
n_aol_layers: 2 # depth of AOL ψ block (channel + token mix per layer)
kappa: 0.9 # contraction factor: per-step Lip_P ≤ (1-α)+α·κ = κ
eta: 1.0 # weighting of L block in P-norm (1.0 = symmetric)
alpha: 1.0 # damping (1.0 = full step)
hidden_size: 512
puzzle_emb_ndim: ${.hidden_size}
# Unused (kept so pretrain.py's __pydantic_extra__ doesn't break)
# pretrain.py's create_model() passes some fields HRM expects; Pydantic 'ignore'
# (default) drops them silently.
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