A Coding Implementation on Microsoft SkillOpt for Instrumented Prompt Optimization, Skill Evolution Analysis, and Baseline Comparison

k = RUN_KNOBS
train_out = run_cli([“python”,”scripts/train.py”,”–config”,CFG,”–split_dir”,SPLIT,
“–optimizer_model”,OPTIMIZER_MODEL,”–target_model”,TARGET_MODEL,”–out_root”,RUN,
*COMMON,
“train.train_size=0″,
f”train.num_epochs={k[‘num_epochs’]}”, f”train.batch_size={k[‘batch_size’]}”,
f”gradient.minibatch_size={k[‘minibatch’]}”, f”gradient.merge_batch_size={k[‘merge_batch’]}”,
f”gradient.analyst_workers={k[‘workers’]}”,
f”optimizer.learning_rate={k[‘lr’]}”, f”optimizer.lr_scheduler={k[‘lr_sched’]}”,
“optimizer.use_slow_update=true”, “optimizer.use_meta_skill=true”,
f”env.workers={k[‘workers’]}”, f”env.limit={k[‘limit’]}”],
“TRAIN (rollout->reflect->aggregate->select->update->gate; slow-update + meta-skill)”)
import pandas as pd, matplotlib.pyplot as plt
hist = json.loads(pathlib.Path(f”{RUN}/history.json”).read_text())
df = pd.json_normalize(hist)
print(“\nhistory.json columns:”, list(df.columns))
def col(*cands):
for c in cands:
for actual in df.columns:
if c in actual.lower(): return actual
return None
c_step = col(“step”)
x = df[c_step] if c_step else range(len(df))
c_tr, c_va = col(“train_acc”,”train_hard”,”train”), col(“val_acc”,”val_hard”,”valid”,”val”)
c_lr, c_tok = col(“edit_budget”,”lr”,”learning_rate”,”budget”), col(“token”,”cost”)
fig, ax = plt.subplots(1, 3, figsize=(16,4))
if c_tr: ax[0].plot(x, df[c_tr], “o-“, label=”train acc”)
if c_va: ax[0].plot(x, df[c_va], “s-“, label=”val acc (gate)”)
if base and base[“hard”] is not None: ax[0].axhline(base[“hard”], ls=”–“, c=”grey”, label=”baseline (seed)”)
ax[0].set_title(“Skill accuracy over steps”); ax[0].set_xlabel(“step”); ax[0].legend(); ax[0].grid(alpha=.3)
if c_lr: ax[1].plot(x, df[c_lr], “d-“, c=”purple”)
ax[1].set_title(“Edit-budget / LR schedule (cosine)”); ax[1].set_xlabel(“step”); ax[1].grid(alpha=.3)
if c_tok: ax[2].plot(x, pd.to_numeric(df[c_tok],errors=”coerce”).cumsum(), c=”darkorange”)
ax[2].set_title(“Cumulative token usage”); ax[2].set_xlabel(“step”); ax[2].grid(alpha=.3)
plt.tight_layout(); plt.savefig(f”{RUN}/training_dashboard.png”, dpi=120); plt.show()