Scaling Laws Loss Vs Training Compute Plot For Different Model Sizes

16:9 landscape orientation, high-resolution publication-quality academic data plot, clean white background, minimalist professional design. X-axis is log-scaled, labeled "Training compute (FLOPs)" with clearly marked log ticks at 1e20, 1e21, 1e22, 1e23, 1e24. Y-axis is linear, labeled "Validation loss (cross-entropy)" with clearly marked decreasing linear ticks at 3.5, 3.0, 2.5, 2.0, 1.5. Four smooth descending trend curves, each paired with a translucent ±1σ shaded band matching the curve color, text labels placed directly near the right tail of each curve: "70M params" in slate gray, "1B params" in muted navy, "10B params" in dusty teal, "70B params" in soft terracotta. Add a warm copper dashed diagonal line labeled "compute-optimal frontier", with small open white circles marking the isoflop crossover points where curves intersect the frontier. Opaque thin-bordered legend box placed at the top-right corner. Bold sans-serif main title at the top center: "Empirical scaling laws: loss vs training compute". Smaller lighter sans-serif subtitle directly below the title: "four model sizes on a fixed data mixture; shaded bands = ±1 std over 3 seeds". Sharp fully legible text, no visual clutter, accurate axis scaling, no distorted labels.