| |
| """Render the top-three non-DAPO reward curves with base-model references.""" |
|
|
| from pathlib import Path |
|
|
| import matplotlib.pyplot as plt |
| import pandas as pd |
|
|
|
|
| ARTIFACTS = Path(__file__).resolve().parent |
| INPUT = ARTIFACTS / "top-three-v49-no-dapo-real-reward.csv" |
| OUTPUT_STEM = ARTIFACTS / "top-three-v49-no-dapo-real-reward" |
|
|
| |
| |
| REFERENCES = { |
| "Agent": {"pass@1": 0.48936170212765956, "pass@16": 0.608843537414966}, |
| } |
|
|
|
|
| def display_label(job: str) -> str: |
| if "entropy3e4-lr2-g16" in job: |
| return "RLOO, entropy 0.0003, LR 2e-6, group 16" |
| if "rloo-entropy3e3" in job: |
| return "RLOO, entropy 0.003, LR 1e-6" |
| if "nodapo-lr2" in job: |
| return "RLOO, LR 2e-6" |
| if "nodapo-lr4" in job: |
| return "RLOO, LR 4e-6" |
| raise ValueError(f"No concise display label defined for {job}") |
|
|
|
|
| def main() -> None: |
| frame = pd.read_csv(INPUT) |
| run_rows = frame[frame["reward_scope"] != "reported_v46_reference"].copy() |
| summaries = ( |
| run_rows.groupby(["dataset", "job"], as_index=False) |
| .agg(step_count=("step", "nunique"), peak=("real_reward", "max")) |
| .query("step_count >= 5") |
| .sort_values(["dataset", "peak"], ascending=[True, False]) |
| .groupby("dataset", as_index=False) |
| .head(3) |
| ) |
| run_rows = run_rows.merge(summaries[["dataset", "job"]], on=["dataset", "job"], how="inner") |
| run_rows["label"] = run_rows["job"].map(display_label) |
|
|
| reference_rows = [] |
| for dataset, values in REFERENCES.items(): |
| for metric, value in values.items(): |
| reference_rows.append( |
| { |
| "dataset": dataset, |
| "job": "Qwen3-Coder-30B-A3B-Instruct", |
| "label": f"Reported v4.6 {metric}", |
| "step": "", |
| "real_reward": value, |
| "reward_scope": "reported_v46_reference", |
| } |
| ) |
| pd.concat([run_rows, pd.DataFrame(reference_rows)], ignore_index=True).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) |
| colors = ["#0072B2", "#D55E00", "#009E73", "#CC79A7", "#E69F00"] |
|
|
| for ax, dataset in zip(axes, ("Instruction-following", "Agent"), strict=True): |
| panel = run_rows[run_rows["dataset"] == dataset] |
| order = ( |
| panel.groupby(["job", "label"], as_index=False)["real_reward"] |
| .max() |
| .sort_values("real_reward", ascending=False) |
| ) |
| for color, row in zip(colors, order.itertuples(index=False), strict=False): |
| series = panel[panel["job"] == row.job].sort_values("step") |
| ax.plot( |
| series["step"], |
| series["real_reward"], |
| color=color, |
| marker="o", |
| markersize=4.5, |
| linewidth=2.2, |
| label=f"{row.label} (peak {row.real_reward:.3f})", |
| zorder=3, |
| ) |
| peak = series.loc[series["real_reward"].idxmax()] |
| ax.scatter( |
| [peak["step"]], |
| [peak["real_reward"]], |
| marker="D", |
| s=58, |
| color=color, |
| edgecolor="white", |
| linewidth=0.8, |
| zorder=4, |
| ) |
|
|
| if dataset in REFERENCES: |
| reference_styles = (("pass@1", (0, (6, 4))), ("pass@16", (0, (2, 2)))) |
| for metric, dash in reference_styles: |
| value = REFERENCES[dataset][metric] |
| ax.axhline( |
| value, |
| color="#4D4D4D", |
| linestyle=dash, |
| linewidth=1.7, |
| alpha=0.9, |
| label=f"Base model {metric} ({value:.3f}, v4.6)", |
| zorder=2, |
| ) |
|
|
| ax.set_title(dataset, pad=12) |
| ax.set_xlabel("Training step") |
| ax.set_xlim(0.15, max(18.85, float(panel["step"].max()) + 0.85)) |
| 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, |
| ncol=1, |
| ) |
|
|
| axes[0].set_ylabel("Verifier outcome reward") |
| fig.suptitle("Top three v4.9 non-DAPO runs by peak real reward", fontsize=21, fontweight="bold", y=0.98) |
| fig.text( |
| 0.5, |
| 0.935, |
| "Unfiltered batch reward; runs have at least five completed steps. " |
| "Agent-panel dashed references are Qwen3-Coder base-model TaskTrove v4.6 evaluations.", |
| ha="center", |
| fontsize=13, |
| color="#444444", |
| ) |
| fig.tight_layout(rect=(0, 0.20, 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() |
|
|
