| |
| """Render DAPO reward curves with at least five recorded steps.""" |
|
|
| from pathlib import Path |
|
|
| import matplotlib.pyplot as plt |
| import pandas as pd |
|
|
|
|
| ARTIFACTS = Path(__file__).resolve().parent |
| INPUT = ARTIFACTS / "top-five-v49-dapo-real-reward.csv" |
| OUTPUT_STEM = ARTIFACTS / "top-five-v49-dapo-real-reward" |
|
|
|
|
| def display_label(job: str) -> str: |
| if "if-v49-dapo-b32" in job: |
| return "DAPO, batch 32" |
| if "agent-v49-sync-r5" in job: |
| return "DAPO, batch 64, zero staleness" |
| raise ValueError(f"No concise display label defined for {job}") |
|
|
|
|
| def main() -> None: |
| frame = pd.read_csv(INPUT) |
| eligible = ( |
| frame.groupby(["dataset", "job"], as_index=False) |
| .agg(step_count=("step", "nunique")) |
| .query("step_count >= 5") |
| ) |
| frame = frame.merge(eligible[["dataset", "job"]], on=["dataset", "job"], how="inner") |
| frame["label"] = frame["job"].map(display_label) |
| frame.to_csv(INPUT, index=False) |
|
|
| plt.rcParams.update( |
| { |
| "font.family": "DejaVu Sans", |
| "font.size": 12, |
| "axes.titlesize": 18, |
| "axes.titleweight": "bold", |
| "axes.labelsize": 13, |
| "legend.fontsize": 10.5, |
| } |
| ) |
| fig, axes = plt.subplots(1, 2, figsize=(15.5, 11.5), sharey=True) |
|
|
| for ax, dataset in zip(axes, ("Instruction-following", "Agent"), strict=True): |
| panel = frame[frame["dataset"] == dataset] |
| for row in panel[["job", "label"]].drop_duplicates().itertuples(index=False): |
| series = panel[panel["job"] == row.job].sort_values("step") |
| peak = series.loc[series["real_reward"].idxmax()] |
| ax.plot( |
| series["step"], |
| series["real_reward"], |
| color="#0072B2", |
| marker="o", |
| markersize=5, |
| linewidth=2.3, |
| label=f"{row.label} (peak {peak['real_reward']:.3f})", |
| zorder=3, |
| ) |
| ax.scatter( |
| [peak["step"]], |
| [peak["real_reward"]], |
| marker="D", |
| s=62, |
| color="#0072B2", |
| edgecolor="white", |
| linewidth=0.8, |
| zorder=4, |
| ) |
|
|
| ax.set_title(dataset, pad=12) |
| ax.set_xlabel("Training step") |
| ax.set_xlim(0.65, max(7.35, float(panel["step"].max()) + 0.35)) |
| ax.set_ylim(0, 0.82) |
| ax.grid(True, color="#CFD5DC", linewidth=0.7, alpha=0.65) |
| ax.set_axisbelow(True) |
| ax.legend(loc="upper center", bbox_to_anchor=(0.5, -0.13), frameon=False) |
|
|
| axes[0].set_ylabel("Verifier outcome reward") |
| fig.suptitle("v4.9 DAPO runs with at least five steps", fontsize=21, fontweight="bold", y=0.98) |
| fig.text( |
| 0.5, |
| 0.935, |
| "Admission-conditioned reward over mixed groups; not an unbiased policy-evaluation score.", |
| ha="center", |
| fontsize=13, |
| color="#444444", |
| ) |
| fig.tight_layout(rect=(0, 0.12, 1, 0.90), w_pad=3.0) |
| fig.savefig(OUTPUT_STEM.with_suffix(".png"), dpi=180, bbox_inches="tight") |
| fig.savefig(OUTPUT_STEM.with_suffix(".svg"), bbox_inches="tight") |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
