| """ |
| Plot training curves from REAL per-step lines emitted by the running HF Job. |
| |
| Every datapoint below appears verbatim in the live job log stream |
| (https://huggingface.co/jobs/testingaccc/69ecfb45d70108f37acdeb50). |
| Nothing is interpolated, smoothed in, or fabricated. |
| |
| Once the final metrics.json uploads at end of training, prefer |
| scripts/plot_from_metrics.py — it reads the full per-step history |
| straight from the model repo. |
| """ |
| import sys |
| from pathlib import Path |
|
|
| import matplotlib |
| matplotlib.use("Agg") |
| import matplotlib.pyplot as plt |
| import numpy as np |
|
|
| |
| DATA = [ |
| (0, -2.50, 50.00), (1, -3.74, 37.50), (2, 5.43, 62.50), |
| (3, 1.50, 37.50), (4, -2.74, 25.00), |
| (10, -1.20, 37.50), (20, -2.25, 25.00), (30, -1.27, 37.50), |
| (40, -2.50, 50.00), (50, 0.56, 50.00), (60, 5.10, 37.50), |
| (70, -2.94, 25.00), (80, 0.25, 62.50), (90, -7.15, 12.50), |
| (100, -3.45, 37.50), (110, 0.38, 37.50), (120, -4.83, 12.50), |
| (130, -1.62, 50.00), (140, -4.38, 37.50), (150, -3.99, 37.50), |
| (160, -3.34, 25.00), (170, 1.38, 50.00), (180, -8.07, 12.50), |
| (190, 0.50, 50.00), (200, 2.62, 62.50), (210, -2.99, 25.00), |
| (220, -2.00, 25.00), (230, -7.99, 12.50), (240, 1.50, 50.00), |
| (250, 11.63, 62.50), (260, -2.47, 25.00), (280, -1.21, 37.50), |
| (290, 5.50, 62.50), (300, -2.33, 25.00), (310, -4.12, 37.50), |
| (320, 5.31, 50.00), (330, -2.45, 50.00), (340, 0.81, 50.00), |
| (350, 3.23, 62.50), (360, -0.62, 62.50), (370, 2.62, 37.50), |
| (380, 8.62, 62.50), (390, 8.85, 62.50), (400, 3.55, 50.00), |
| (410, -3.25, 37.50), (420, -6.20, 25.00), (430, 1.78, 37.50), |
| (440, -5.33, 25.00), (450, 2.40, 62.50), |
| (1200, 1.82, 37.50), (1210, -1.38, 37.50), (1220, -0.10, 25.00), |
| (1230, -1.35, 37.50), (1240, -3.06, 25.00), (1250, -3.19, 25.00), |
| (1260, -7.88, 12.50), (1270, 1.88, 37.50), (1280, 3.92, 50.00), |
| (1290, -3.24, 25.00), (1300, 6.70, 50.00), (1310, -3.17, 25.00), |
| (1320, 5.65, 62.50), (1330, 2.50, 62.50), (1340, -6.25, 25.00), |
| (1350, -6.25, 25.00), (1360, -6.25, 25.00), (1370, 2.25, 62.50), |
| (1380, -4.88, 25.00), (1390, -6.15, 25.00), (1400, 0.86, 25.00), |
| (1410, 3.62, 50.00), (1420, -1.38, 37.50), (1430, -6.17, 25.00), |
| (1710, -4.38, 37.50), (1720, -7.75, 12.50), (1730, -0.48, 37.50), |
| (1740, -1.25, 37.50), (1750, -1.25, 37.50), (1760, -0.35, 37.50), |
| (1770, 8.77, 62.50), (1780, 2.51, 62.50), (1790, 0.06, 25.00), |
| (1800, 0.76, 50.00), (1810, 5.68, 62.50), (1820, 1.50, 50.00), |
| (1830, -7.03, 12.50), (1840, 0.62, 50.00), (1850, -7.79, 12.50), |
| (1860, 0.48, 50.00), (1870, -2.88, 25.00), (1880, 0.75, 50.00), |
| (1890, -9.93, 0.00), (1900, -5.12, 25.00), (1910, -2.38, 25.00), |
| (1920, -1.25, 37.50), (1930, -3.11, 25.00), (1940, 11.91, 62.50), |
| (1950, -9.92, 0.00), (1960, -1.52, 37.50), |
| ] |
|
|
|
|
| def rolling_mean(arr, window): |
| arr = np.asarray(arr, dtype=float) |
| if len(arr) < window: |
| return arr |
| out = np.empty_like(arr) |
| cs = np.cumsum(np.insert(arr, 0, 0)) |
| for i in range(len(arr)): |
| lo = max(0, i - window + 1) |
| out[i] = (cs[i + 1] - cs[lo]) / (i - lo + 1) |
| return out |
|
|
|
|
| def plot(out_path: str = "training_curves.png"): |
| steps = np.array([r[0] for r in DATA]) |
| rewards = np.array([r[1] for r in DATA]) |
| accs = np.array([r[2] for r in DATA]) |
|
|
| plt.style.use("dark_background") |
| fig, axes = plt.subplots(2, 2, figsize=(14, 9)) |
| fig.suptitle( |
| f"Conflict Arbitration Agent - GRPO training (real per-step data, n={len(steps)})", |
| fontsize=14, fontweight="bold", color="#e6e6f0", |
| ) |
|
|
| |
| ax = axes[0, 0] |
| ax.scatter(steps, rewards, alpha=0.5, c="#8be9d6", s=30, label="per-step reward") |
| if len(rewards) >= 5: |
| ax.plot(steps, rolling_mean(rewards, 5), color="#ff79c6", linewidth=2.5, |
| label="rolling avg (window=5)") |
| ax.axhline(0, color="#666", linestyle="--", linewidth=1, alpha=0.7) |
| ax.set_title("Average reward over training step", color="#e6e6f0") |
| ax.set_xlabel("Training step") |
| ax.set_ylabel("Reward (mean of 8 GRPO rollouts)") |
| ax.legend(loc="lower right", framealpha=0.3) |
| ax.grid(True, alpha=0.15) |
|
|
| |
| ax = axes[0, 1] |
| ax.scatter(steps, accs, alpha=0.5, c="#50fa7b", s=30, label="per-step accuracy") |
| if len(accs) >= 5: |
| ax.plot(steps, rolling_mean(accs, 5), color="#f1fa8c", linewidth=2.5, |
| label="rolling avg (window=5)") |
| ax.axhline(33.3, color="#ff5555", linestyle="--", linewidth=1.5, alpha=0.7, |
| label="random baseline (33.3%)") |
| ax.set_title("Arbitration accuracy over training step", color="#e6e6f0") |
| ax.set_xlabel("Training step") |
| ax.set_ylabel("Accuracy (%)") |
| ax.set_ylim(-5, 105) |
| ax.legend(loc="lower right", framealpha=0.3) |
| ax.grid(True, alpha=0.15) |
|
|
| |
| ax = axes[1, 0] |
| early_mask = steps <= 500 |
| late_mask = steps >= 1700 |
| bins = np.linspace(rewards.min() - 0.5, rewards.max() + 0.5, 16) |
| ax.hist(rewards[early_mask], bins=bins, alpha=0.6, color="#ff5555", |
| label=f"early steps 0-450 (n={int(early_mask.sum())})") |
| ax.hist(rewards[late_mask], bins=bins, alpha=0.6, color="#50fa7b", |
| label=f"late steps 1710-1960 (n={int(late_mask.sum())})") |
| ax.axvline(rewards[early_mask].mean(), color="#ff5555", linestyle="--", linewidth=2, |
| label=f"early mean = {rewards[early_mask].mean():+.2f}") |
| ax.axvline(rewards[late_mask].mean(), color="#50fa7b", linestyle="--", linewidth=2, |
| label=f"late mean = {rewards[late_mask].mean():+.2f}") |
| ax.set_title("Reward distribution: early vs late training", color="#e6e6f0") |
| ax.set_xlabel("Reward") |
| ax.set_ylabel("Frequency") |
| ax.legend(loc="upper left", framealpha=0.3, fontsize=9) |
| ax.grid(True, alpha=0.15) |
|
|
| |
| ax = axes[1, 1] |
| ax.axis("off") |
| early_r = rewards[early_mask] |
| late_r = rewards[late_mask] |
| early_a = accs[early_mask] |
| late_a = accs[late_mask] |
| pos = int((rewards > 0).sum()) |
| above_chance = int((accs > 33.3).sum()) |
| text = f"""TRAINING SUMMARY (real log data, no interpolation) |
| {'='*46} |
| Datapoints logged: {len(steps)} |
| Step range covered: {steps[0]} -> {steps[-1]} |
| Curriculum phase: 1 throughout |
| Hardware: A10G-small via HF Jobs |
| Job ID: 69ecfb45d70108f37acdeb50 |
| |
| REWARD |
| Early (steps 0-450): mean {early_r.mean():+.2f} |
| Late (steps 1710-1960): mean {late_r.mean():+.2f} |
| Improvement (late-early): {late_r.mean() - early_r.mean():+.2f} |
| Best step: {rewards.max():+.2f} (step {steps[int(np.argmax(rewards))]}) |
| Worst step: {rewards.min():+.2f} (step {steps[int(np.argmin(rewards))]}) |
| Positive-reward steps: {pos}/{len(rewards)} ({100*pos/len(rewards):.0f}%) |
| |
| ACCURACY |
| Early (steps 0-450): mean {early_a.mean():.1f}% |
| Late (steps 1710-1960): mean {late_a.mean():.1f}% |
| Best: {accs.max():.1f}% |
| Random baseline: 33.3% |
| Above-chance steps: {above_chance}/{len(accs)} ({100*above_chance/len(accs):.0f}%) |
| |
| NOTES |
| - High reward variance is expected: 8 stochastic rollouts/step |
| at temperature 0.9 with sparse, contrastive reward. |
| - Curriculum did not advance to phase 2; threshold was 70%. |
| - Final per-step metrics.json will overwrite this on completion. |
| """ |
| ax.text(0.0, 0.98, text, transform=ax.transAxes, fontsize=9, |
| verticalalignment="top", fontfamily="monospace", color="#c8c8e8") |
|
|
| plt.tight_layout() |
| plt.savefig(out_path, dpi=150, bbox_inches="tight", facecolor="#0a0a14") |
| print(f"saved {out_path} ({len(steps)} real datapoints)") |
|
|
| |
| print(f"early reward mean: {early_r.mean():+.3f}") |
| print(f"late reward mean: {late_r.mean():+.3f}") |
| print(f"early acc mean: {early_a.mean():.2f}%") |
| print(f"late acc mean: {late_a.mean():.2f}%") |
| print(f"random baseline: 33.30%") |
|
|
|
|
| if __name__ == "__main__": |
| out = sys.argv[1] if len(sys.argv) > 1 else "training_curves.png" |
| plot(out) |
|
|
