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Phase 7: Build reward-curve plot + comparison artifact.
Inputs (any subset that exists):
results/training_log.jsonl # per-step rewards from GRPO
results/baseline_zero_shot_real_subset.json
results/baseline_cot_real_subset.json
results/baseline_zero_shot_stub.json
results/baseline_cot_stub.json
results/trained_agent.json # eval of the trained agent (Phase 6)
Outputs:
results/comparison.json
docs/reward_curve.png
docs/baseline_comparison.png
"""
from __future__ import annotations
import argparse
import json
from pathlib import Path
from typing import Any, Dict, List
def load_json(p: Path) -> dict | None:
if not p.exists():
return None
try:
return json.loads(p.read_text(encoding="utf-8"))
except Exception as e:
print(f"[plot] WARN failed to load {p}: {e}")
return None
def load_jsonl(p: Path) -> List[dict]:
if not p.exists():
return []
rows: List[dict] = []
for line in p.read_text(encoding="utf-8").splitlines():
line = line.strip()
if not line:
continue
try:
rows.append(json.loads(line))
except Exception:
pass
return rows
def smooth(values: List[float], window: int = 10) -> List[float]:
out: List[float] = []
for i in range(len(values)):
lo = max(0, i - window + 1)
out.append(sum(values[lo:i + 1]) / max(1, i + 1 - lo))
return out
def main():
p = argparse.ArgumentParser()
p.add_argument("--results-dir", default="results")
p.add_argument("--docs-dir", default="docs")
args = p.parse_args()
res = Path(args.results_dir)
docs = Path(args.docs_dir)
docs.mkdir(parents=True, exist_ok=True)
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
# ---- 1. reward curve ----
log_rows = load_jsonl(res / "training_log.jsonl")
if log_rows:
rewards = [r["reward"]["total"] for r in log_rows if "reward" in r]
smoothed = smooth(rewards, window=20)
fig, ax = plt.subplots(figsize=(8, 4.5))
ax.plot(rewards, alpha=0.25, label="raw reward")
ax.plot(smoothed, linewidth=2, label="rolling avg (20)")
ax.set_xlabel("training rollout #")
ax.set_ylabel("reward")
ax.set_title("GRPO training reward curve · PromptOps Arena")
ax.grid(alpha=0.3)
ax.legend()
fig.tight_layout()
out = docs / "reward_curve.png"
fig.savefig(out, dpi=140)
plt.close(fig)
print(f"[plot] wrote {out} ({len(rewards)} points)")
else:
print("[plot] no training_log.jsonl yet -> skip reward curve")
# ---- 2. baseline comparison ----
files = {
"zero_shot (real LLM)": res / "baseline_zero_shot_real.json",
"cot (real LLM)": res / "baseline_cot_real.json",
"untrained 1.5B agent (real LLM, 3 turns)": res / "baseline_untrained_real.json",
"trained 1.5B agent (real LLM, 2 turns)": res / "trained_agent.json",
}
# fall back to the smaller subset files if the wider-n versions don't exist
fallback = {
"zero_shot (real LLM)": res / "baseline_zero_shot_real_subset.json",
"cot (real LLM)": res / "baseline_cot_real_subset.json",
}
for k, p in fallback.items():
if not files[k].exists() and p.exists():
files[k] = p
rows: Dict[str, Dict[str, Any]] = {}
for label, path in files.items():
d = load_json(path)
if d is None:
continue
ov = d.get("overall", {})
rows[label] = {
"n": ov.get("n", 0),
"correct": ov.get("correct", 0),
"format": ov.get("format", 0),
"mean_reward": ov.get("mean_reward", 0.0),
"by_type": d.get("by_type", {}),
"backend": d.get("llm_backend", "unknown"),
}
comparison = {
"policies": rows,
"ranking_by_mean_reward": sorted(
rows.items(),
key=lambda kv: kv[1]["mean_reward"],
reverse=True,
),
}
(res / "comparison.json").write_text(
json.dumps(comparison, indent=2), encoding="utf-8"
)
print(f"[plot] wrote {res/'comparison.json'}")
# ---- 3. comparison bar chart ----
if rows:
labels = list(rows.keys())
means = [rows[l]["mean_reward"] for l in labels]
accs = [rows[l]["correct"] / max(1, rows[l]["n"]) for l in labels]
fig, axes = plt.subplots(1, 2, figsize=(11, 4.5))
axes[0].barh(labels, means, color="#4c72b0")
axes[0].set_xlabel("mean reward")
axes[0].set_title("Mean reward by policy")
axes[0].grid(axis="x", alpha=0.3)
axes[0].invert_yaxis()
axes[1].barh(labels, accs, color="#55a868")
axes[1].set_xlabel("fraction correct")
axes[1].set_title("Correctness by policy")
axes[1].set_xlim(0, 1)
axes[1].grid(axis="x", alpha=0.3)
axes[1].invert_yaxis()
fig.tight_layout()
out = docs / "baseline_comparison.png"
fig.savefig(out, dpi=140)
plt.close(fig)
print(f"[plot] wrote {out}")
if __name__ == "__main__":
main()
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