diff --git a/miniseries.sh b/miniseries.sh
index 9287def..077418a 100644
--- a/miniseries.sh
+++ b/miniseries.sh
@@ -27,8 +27,11 @@ RESULTS_DIR="$NANOCHAT_BASE_DIR/jan7_miniseries_results"
 mkdir -p "$RESULTS_DIR"
 RESULTS_FILE="$RESULTS_DIR/results.csv"
 
-# Write CSV header
-echo "depth,model_dim,num_params,num_scaling_params,num_iterations,tokens_trained,param_data_ratio,val_bpb,core_score,train_time_sec" > "$RESULTS_FILE"
+# Write CSV header only if file doesn't exist
+if [ ! -f "$RESULTS_FILE" ]; then
+    echo "depth,model_dim,num_params,num_scaling_params,num_iterations,tokens_trained,param_data_ratio,val_bpb,core_score,train_time_sec" > "$RESULTS_FILE"
+fi
+
 log() {
     echo "[$(date '+%Y-%m-%d %H:%M:%S')] $1"
 }
diff --git a/scaling_laws.sh b/scaling_laws.sh
new file mode 100644
index 0000000..102ba11
--- /dev/null
+++ b/scaling_laws.sh
@@ -0,0 +1,115 @@
+#!/bin/bash
+
+FLOPS_BUDGETS=(
+    1e18
+    3e18
+    6e18
+)
+DEPTHS=(8 10 12 14 16 18 20)
+NPROC_PER_NODE="${NPROC_PER_NODE:-8}"
+WANDB_RUN="${WANDB_RUN:-scaling}"
+EVAL_TOKENS=$((100 * 524288))  # ~100M tokens for final eval (default is ~10M)
+
+export OMP_NUM_THREADS=1
+export NANOCHAT_BASE_DIR="${NANOCHAT_BASE_DIR:-$HOME/.cache/nanochat}"
+source .venv/bin/activate
+
+RESULTS_DIR="$NANOCHAT_BASE_DIR/scaling_laws_results"
+mkdir -p "$RESULTS_DIR"
+RESULTS_FILE="$RESULTS_DIR/results.csv"
+
+# Write CSV header only if file doesn't exist
+if [ ! -f "$RESULTS_FILE" ]; then
+    echo "flops_budget,depth,model_dim,num_params,num_scaling_params,num_iterations,tokens_trained,param_data_ratio,val_bpb,core_score,train_time_sec" > "$RESULTS_FILE"
+fi
+
+log() {
+    echo "[$(date '+%Y-%m-%d %H:%M:%S')] $1"
+}
+
+# Check if a run already exists in results
+run_exists() {
+    local flops=$1
+    local depth=$2
+    grep -q "^${flops},${depth}," "$RESULTS_FILE" 2>/dev/null
+}
+
+# =============================================================================
+# Main Loop
+# =============================================================================
+
+for flops in "${FLOPS_BUDGETS[@]}"; do
+    log "=============================================="
+    log "Compute budget: $flops FLOPs"
+    log "=============================================="
+
+    for d in "${DEPTHS[@]}"; do
+
+        # Skip if already completed
+        if run_exists "$flops" "$d"; then
+            log "Skipping d=$d at $flops FLOPs (already in results)"
+            continue
+        fi
+
+        log "Training d=$d at $flops FLOPs..."
+
+        # Unique tag for this run
+        TAG="scaling_${flops}_d${d}"
+
+        # Record start time
+        START_TIME=$(date +%s)
+
+        # Train the model with fixed flops budget
+        # The script will auto-calculate num_iterations to hit target_flops
+        # CORE eval happens once at the end (999999 ensures only final step)
+        torchrun --standalone --nproc_per_node=$NPROC_PER_NODE -m scripts.base_train -- \
+            --depth=$d \
+            --target_flops=$flops \
+            --target_param_data_ratio=-1 \
+            --run="${WANDB_RUN}_${TAG}" \
+            --model_tag="${TAG}" \
+            --eval_tokens=$EVAL_TOKENS \
+            --core_metric_every=999999 \
+            --core_metric_max_per_task=-1 \
+            --sample_every=-1 \
+            --save_every=-1 \
+            2>&1 | tee "$RESULTS_DIR/${TAG}_train.log"
+
+        END_TIME=$(date +%s)
+        TRAIN_TIME=$((END_TIME - START_TIME))
+
+        # Extract training stats from the log
+        LOG_FILE="$RESULTS_DIR/${TAG}_train.log"
+        NUM_PARAMS=$(grep "Number of parameters:" "$LOG_FILE" | tail -1 | grep -oP '[\d,]+' | head -1 | tr -d ',')
+        NUM_SCALING_PARAMS=$(grep "Number of parameters:" "$LOG_FILE" | tail -1 | grep -oP 'scaling: [\d,]+' | grep -oP '[\d,]+' | tr -d ',')
+        NUM_ITERS=$(grep "Calculated number of iterations" "$LOG_FILE" | tail -1 | sed 's/.*: //' | tr -d ',')
+        # Calculate tokens trained (iterations * batch_size, default 524288)
+        TOKENS_TRAINED=$((NUM_ITERS * 524288))
+        # Param:data ratio (using scaling params per Kaplan et al.)
+        PARAM_DATA_RATIO=$(python -c "print(f'{$TOKENS_TRAINED / $NUM_SCALING_PARAMS:.2f}')")
+        # Model dim
+        MODEL_DIM=$((d * 64))
+        # Val BPB from final eval
+        VAL_BPB=$(grep "Validation bpb:" "$LOG_FILE" | tail -1 | grep -oP '[\d.]+$')
+
+        # Extract CORE score from training log (evaluated on final step)
+        CORE_SCORE=$(grep "CORE metric:" "$LOG_FILE" | tail -1 | awk '{print $NF}')
+        if [ -z "$CORE_SCORE" ]; then
+            log "WARNING: Could not extract CORE score for d=$d"
+            CORE_SCORE="0.0"
+        fi
+
+        log "  Params: $NUM_PARAMS, Iters: $NUM_ITERS, Ratio: $PARAM_DATA_RATIO, Val BPB: $VAL_BPB, CORE: $CORE_SCORE"
+
+        # Append to CSV
+        echo "$flops,$d,$MODEL_DIM,$NUM_PARAMS,$NUM_SCALING_PARAMS,$NUM_ITERS,$TOKENS_TRAINED,$PARAM_DATA_RATIO,$VAL_BPB,$CORE_SCORE,$TRAIN_TIME" >> "$RESULTS_FILE"
+    done
+done
+
+log "=============================================="
+log "Scaling Laws Sweep Complete"
+log "=============================================="
+log "Results saved to: $RESULTS_FILE"
+echo ""
+echo "Results:"
+column -t -s',' "$RESULTS_FILE"