File size: 3,248 Bytes
f227f52 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 | #!/usr/bin/env python3
"""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()
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