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diff --git a/scripts/run_weak_reg.sbatch b/scripts/run_weak_reg.sbatch
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+#!/bin/bash
+#SBATCH --job-name=snn_weak_reg
+#SBATCH --account=bfqt-delta-gpu
+#SBATCH --partition=gpuA40x4
+#SBATCH --nodes=1
+#SBATCH --ntasks=1
+#SBATCH --cpus-per-task=8
+#SBATCH --gpus-per-node=1
+#SBATCH --mem=64G
+#SBATCH --time=48:00:00
+#SBATCH --output=runs/slurm_logs/%j_weak_reg.out
+#SBATCH --error=runs/slurm_logs/%j_weak_reg.err
+
+# ============================================================
+# Experiment 1: Weaker Regularization (lambda_reg=0.01)
+# ============================================================
+# Hypothesis: The current lambda_reg=0.3 is too strong, causing
+# the Lyapunov penalty to dominate the CE loss and prevent learning.
+#
+# With lambda_reg=0.01:
+# - Penalty contribution: 0.01 * 4 = 0.04 (vs CE ~4.6)
+# - Network can still learn while gently being regularized
+# ============================================================
+
+set -e
+
+PROJECT_DIR="/projects/bfqt/users/yurenh2/ml-projects/snn-training"
+cd "$PROJECT_DIR"
+
+mkdir -p runs/slurm_logs data
+
+echo "============================================================"
+echo "WEAK REGULARIZATION Experiment (lambda_reg=0.01)"
+echo "Job ID: $SLURM_JOB_ID | Node: $SLURM_NODELIST"
+echo "Start: $(date)"
+echo "============================================================"
+nvidia-smi --query-gpu=name,memory.total --format=csv,noheader
+echo "============================================================"
+
+python files/experiments/depth_scaling_benchmark.py \
+    --dataset cifar100 \
+    --depths 4 8 12 16 \
+    --T 4 \
+    --epochs 150 \
+    --batch_size 128 \
+    --lr 0.001 \
+    --lambda_reg 0.01 \
+    --lambda_target -0.1 \
+    --reg_type squared \
+    --warmup_epochs 20 \
+    --data_dir ./data \
+    --out_dir runs/depth_scaling_weak_reg \
+    --device cuda
+
+echo "============================================================"
+echo "Finished: $(date)"
+echo "============================================================"