| |
| """Plot fixed-holdout validation for the ten selected calendar RL candidates.""" |
|
|
| from pathlib import Path |
|
|
| import matplotlib.pyplot as plt |
| import pandas as pd |
|
|
|
|
| ARTIFACTS = Path(__file__).resolve().parent |
| INPUT = ARTIFACTS / "top-ten-checkpoint-fixed-validation.csv" |
| OUTPUT_PNG = ARTIFACTS / "top-ten-checkpoint-fixed-validation.png" |
| OUTPUT_SVG = ARTIFACTS / "top-ten-checkpoint-fixed-validation.svg" |
|
|
| LABELS = { |
| "q3c-rl-calendar-if-v47-r6": "Historical R6 (v2 train verifier)", |
| "q3c-rl-calendar-if-v49-nodapo-lr2-r1": "RLOO, LR 2e-6", |
| "q3c-rl-calendar-if-v49-nodapo-lr4-r1": "RLOO, LR 4e-6", |
| "q3c-rl-calendar-if-v49-dapo-b32-r1": "DAPO, batch 32", |
| "q3c-rl-calendar-if-v49-rloo-entropy3e3-stale0-lr1-r1": "RLOO, entropy 0.003, LR 1e-6", |
| "q3c-rl-calendar-if-v49-rloo-entropy3e5-lr2-g16-constdenom-warmup4-r1": ( |
| "RLOO, constant denominator, group 16" |
| ), |
| "q3c-rl-calendar-agent-v49-sync-r5": "Synchronous RLOO", |
| "q3c-rl-calendar-agent-v49-shaped-nodapo-lr2-r1": "Shaped RLOO, LR 2e-6", |
| "q3c-rl-calendar-agent-v49-shaped-nodapo-lr4-r1": "Shaped RLOO, LR 4e-6", |
| "q3c-rl-calendar-agent-v49-shaped-rloo-entropy3e3-stale0-lr1-r1": ( |
| "Shaped RLOO, entropy 0.003, LR 1e-6" |
| ), |
| "q3c-rl-calendar-agent-v49-shaped-rloo-entropy3e5-lr2-g16-constdenom-warmup4-r1": ( |
| "Shaped RLOO, constant denominator, group 16" |
| ), |
| } |
|
|
|
|
| def main() -> None: |
| frame = pd.read_csv(INPUT) |
| figure, axes = plt.subplots(1, 2, figsize=(18, 11), sharey=True) |
| colors = plt.get_cmap("tab10").colors |
|
|
| for axis, dataset, title in zip( |
| axes, |
| ("instruction-following", "agent"), |
| ("Instruction-following calendar", "Agent calendar"), |
| strict=True, |
| ): |
| subset = frame[frame["dataset"] == dataset] |
| base = subset[subset["source_job"] == "Qwen/Qwen3-Coder-30B-A3B-Instruct"].iloc[0] |
| axis.axhline( |
| base["val_avg_score"], |
| color="#5f6368", |
| linestyle=(0, (5, 3)), |
| linewidth=2, |
| label=f"Base model ({base['val_avg_score']:.3f})", |
| ) |
|
|
| jobs = [job for job in subset["source_job"].drop_duplicates() if job != base["source_job"]] |
| for index, job in enumerate(jobs): |
| series = subset[subset["source_job"] == job].sort_values("checkpoint_step") |
| historical = job == "q3c-rl-calendar-if-v47-r6" |
| color = "#8a8a8a" if historical else colors[index % len(colors)] |
| line_style = ":" if historical else "-" |
| axis.plot( |
| series["checkpoint_step"], |
| series["val_avg_score"], |
| marker="o", |
| markersize=6, |
| linewidth=2.5, |
| linestyle=line_style, |
| color=color, |
| label=LABELS[job], |
| ) |
| peak = series.loc[series["val_avg_score"].idxmax()] |
| axis.scatter( |
| [peak["checkpoint_step"]], |
| [peak["val_avg_score"]], |
| marker="D", |
| s=90, |
| color=color, |
| edgecolor="white", |
| linewidth=1.2, |
| zorder=4, |
| ) |
|
|
| axis.set_title(title, fontsize=20, fontweight="bold", pad=14) |
| axis.set_xlabel("Checkpoint step", fontsize=15) |
| axis.set_xticks(sorted(subset["checkpoint_step"].unique())) |
| axis.set_xlim(-0.6, subset["checkpoint_step"].max() + 0.8) |
| axis.set_ylim(0, 0.52) |
| axis.grid(True, alpha=0.25) |
| axis.legend(loc="upper center", bbox_to_anchor=(0.5, -0.15), frameon=False, fontsize=11, ncol=2) |
|
|
| axes[0].set_ylabel("Fixed-holdout mean reward", fontsize=15) |
| figure.suptitle("Qwen3-Coder calendar RL checkpoint validation", fontsize=25, fontweight="bold", y=0.98) |
| figure.text( |
| 0.5, |
| 0.935, |
| "Seed-42, 128-task holdouts, temperature 0. All RL candidates trained on sources containing the later holdout; R6 also used the older v2 verifier.", |
| ha="center", |
| fontsize=14, |
| color="#4f4f4f", |
| ) |
| figure.text(0.5, 0.055, "Diamonds mark each curve's peak.", ha="center", fontsize=12, color="#4f4f4f") |
| figure.subplots_adjust(top=0.86, bottom=0.27, left=0.08, right=0.98, wspace=0.10) |
| figure.savefig(OUTPUT_PNG, dpi=180) |
| figure.savefig(OUTPUT_SVG) |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
