diff --git a/scripts/tools/get_best_lr_per_task_from_wandb.py b/scripts/tools/get_best_lr_per_task_from_wandb.py
new file mode 100644
index 000000000..873d18126
--- /dev/null
+++ b/scripts/tools/get_best_lr_per_task_from_wandb.py
@@ -0,0 +1,181 @@
+"""Find the best LR (and corresponding val + test) per task in a sweep of wandb runs.
+
+Given a prefix that matches multiple wandb runs (each differing in probe LR), for each
+downstream eval task report:
+  - the LR from the run with the highest val score
+  - that val score
+  - the corresponding test score
+
+Usage:
+    python -m scripts.tools.get_best_lr_per_task_from_wandb \
+        -p my_project --run-prefix my_experiment_step300000
+"""
+
+import argparse
+import csv
+import re
+
+import wandb
+
+WANDB_ENTITY = "eai-ai2"
+
+# Match the eval LR encoded in run names like
+# "..._pre_trained_lr0.0005_ptmean" or "..._dataset_lr0.01_ptmax".
+_LR_FROM_NAME_RE = re.compile(r"_lr([0-9.eE+-]+)_pt")
+
+
+def _lr_from_run_name(name: str) -> float | None:
+    matches = _LR_FROM_NAME_RE.findall(name)
+    if not matches:
+        return None
+    try:
+        return float(matches[-1])
+    except ValueError:
+        return None
+
+
+def _normalize_eval_key(key: str) -> str:
+    """eval_other/... -> eval/... so we can compare across both namespaces."""
+    if key.startswith("eval_other/"):
+        return key.replace("eval_other/", "eval/", 1)
+    return key
+
+
+def _get_test_value(
+    run: wandb.apis.public.Run, task: str, val_key: str
+) -> tuple[float | None, str | None]:
+    """Look up the test value matching a normalized validation key."""
+    test_key = val_key.replace("eval/", "eval/test/", 1)
+    candidates = [
+        test_key,
+        test_key.replace("eval/", "eval_other/", 1),
+        f"eval/test/{task}",
+    ]
+    for k in candidates:
+        v = run.summary.get(k)
+        if v is not None:
+            return float(v), k
+    return None, None
+
+
+def _task_config(run: wandb.apis.public.Run, task: str) -> dict:
+    return (
+        run.config.get("trainer", {})
+        .get("callbacks", {})
+        .get("downstream_evaluator", {})
+        .get("tasks", {})
+        .get(task, {})
+    )
+
+
+def get_best_per_task(project: str, run_prefix: str) -> dict[str, dict]:
+    """Return per-task best-val info across runs matching `run_prefix`."""
+    api = wandb.Api()
+    wandb_path = f"{WANDB_ENTITY}/{project}"
+    runs = [
+        r for r in api.runs(wandb_path, lazy=False) if r.name.startswith(run_prefix)
+    ]
+    print(f"Found {len(runs)} runs with prefix '{run_prefix}'")
+    for r in runs:
+        print(f"  {r.name} ({r.id})")
+
+    # task -> {"val": float, "run": Run, "val_key": str}
+    best: dict[str, dict] = {}
+    for run in runs:
+        for key, value in run.summary.items():
+            if not (key.startswith("eval/") or key.startswith("eval_other/")):
+                continue
+            if key.startswith("eval/test/") or key.startswith("eval_other/test/"):
+                continue
+            if not isinstance(value, int | float):
+                continue
+            normalized = _normalize_eval_key(key)
+            parts = normalized.split("/")
+            if len(parts) < 2:
+                continue
+            task = parts[1]
+            if task not in best or value > best[task]["val"]:
+                best[task] = {"val": float(value), "run": run, "val_key": normalized}
+
+    results: dict[str, dict] = {}
+    for task, info in best.items():
+        run = info["run"]
+        cfg = _task_config(run, task)
+        test_val, test_key = _get_test_value(run, task, info["val_key"])
+        lr = cfg.get("ft_lr") or cfg.get("probe_lr")
+        if lr is None:
+            lr = _lr_from_run_name(run.name)
+        results[task] = {
+            "lr": lr,
+            "val": info["val"],
+            "val_key": info["val_key"],
+            "test": test_val,
+            "test_key": test_key,
+            "pooling_type": cfg.get("pooling_type"),
+            "norm_stats_from_pretrained": cfg.get("norm_stats_from_pretrained"),
+            "run_id": run.id,
+            "run_name": run.name,
+        }
+    return results
+
+
+def _print_table(results: dict[str, dict]) -> None:
+    header = f"{'task':<40} {'lr':>12} {'val':>10} {'test':>10}  run"
+    print("\n" + header)
+    print("-" * len(header))
+    for task in sorted(results):
+        r = results[task]
+        lr = f"{r['lr']:.2e}" if isinstance(r["lr"], int | float) else "—"
+        val = f"{r['val']:.4f}"
+        test = f"{r['test']:.4f}" if r["test"] is not None else "—"
+        print(f"{task:<40} {lr:>12} {val:>10} {test:>10}  {r['run_name']}")
+
+
+def _save_csv(results: dict[str, dict], path: str) -> None:
+    fields = [
+        "task",
+        "lr",
+        "val",
+        "test",
+        "val_key",
+        "test_key",
+        "pooling_type",
+        "norm_stats_from_pretrained",
+        "run_id",
+        "run_name",
+    ]
+    with open(path, "w", newline="") as f:
+        w = csv.DictWriter(f, fieldnames=fields)
+        w.writeheader()
+        for task in sorted(results):
+            row = {"task": task, **results[task]}
+            w.writerow({k: row.get(k) for k in fields})
+    print(f"\nWrote {path}")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument(
+        "-p", "--project", required=True, help="W&B project under eai-ai2"
+    )
+    parser.add_argument(
+        "--run-prefix",
+        required=True,
+        help="Prefix shared by the runs in the sweep (typically the run name with the LR suffix stripped).",
+    )
+    parser.add_argument(
+        "-o",
+        "--output",
+        default=None,
+        help="Optional CSV output path (default: {run_prefix}_best_lr.csv).",
+    )
+    args = parser.parse_args()
+
+    results = get_best_per_task(args.project, args.run_prefix)
+    if not results:
+        print("No matching tasks found.")
+        raise SystemExit(1)
+
+    _print_table(results)
+    out = args.output or f"{args.run_prefix}_best_lr.csv"
+    _save_csv(results, out)