Field study: updating steps vs score (131 runs), with pre-registered recheck
Browse files
analysis/2026-10-19/field-gradients.json
ADDED
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@@ -0,0 +1,1975 @@
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+
{
|
| 1839 |
+
"user": "NoeFlandre",
|
| 1840 |
+
"submission_id": "workbench-gym-v3",
|
| 1841 |
+
"run_id": "99908f0c348c181c87f9a087",
|
| 1842 |
+
"solved": 3,
|
| 1843 |
+
"steps": 202,
|
| 1844 |
+
"logged": 96,
|
| 1845 |
+
"grad_steps": 30,
|
| 1846 |
+
"grad_norm_sum": 14.745,
|
| 1847 |
+
"mean_reward": 0.882,
|
| 1848 |
+
"scores": {
|
| 1849 |
+
"office-white-collar": 2,
|
| 1850 |
+
"software-engineering": 1
|
| 1851 |
+
}
|
| 1852 |
+
},
|
| 1853 |
+
{
|
| 1854 |
+
"user": "distributedtensor",
|
| 1855 |
+
"submission_id": "posttrain-m4-c",
|
| 1856 |
+
"run_id": "50f8c181ca24d7a72e33bec4",
|
| 1857 |
+
"solved": 3,
|
| 1858 |
+
"steps": 134,
|
| 1859 |
+
"logged": 70,
|
| 1860 |
+
"grad_steps": 43,
|
| 1861 |
+
"grad_norm_sum": 23.988,
|
| 1862 |
+
"mean_reward": 0.672,
|
| 1863 |
+
"scores": {
|
| 1864 |
+
"finance-economics": 1,
|
| 1865 |
+
"office-white-collar": 2
|
| 1866 |
+
}
|
| 1867 |
+
},
|
| 1868 |
+
{
|
| 1869 |
+
"user": "rca-umb",
|
| 1870 |
+
"submission_id": "five-desks-v10",
|
| 1871 |
+
"run_id": "ab4d729e0946348182d0ab1c",
|
| 1872 |
+
"solved": 2,
|
| 1873 |
+
"steps": 76,
|
| 1874 |
+
"logged": 51,
|
| 1875 |
+
"grad_steps": 31,
|
| 1876 |
+
"grad_norm_sum": 12.358,
|
| 1877 |
+
"mean_reward": 0.309,
|
| 1878 |
+
"scores": {
|
| 1879 |
+
"industrial-physical-systems": 1,
|
| 1880 |
+
"office-white-collar": 1
|
| 1881 |
+
}
|
| 1882 |
+
},
|
| 1883 |
+
{
|
| 1884 |
+
"user": "Syndicate604",
|
| 1885 |
+
"submission_id": "evidence-desk-math-v4-rerun",
|
| 1886 |
+
"run_id": "df794d8796d63710a208f9ba",
|
| 1887 |
+
"solved": 3,
|
| 1888 |
+
"steps": 195,
|
| 1889 |
+
"logged": 195,
|
| 1890 |
+
"grad_steps": 105,
|
| 1891 |
+
"grad_norm_sum": 77.371,
|
| 1892 |
+
"mean_reward": 0.7,
|
| 1893 |
+
"scores": {
|
| 1894 |
+
"industrial-physical-systems": 2,
|
| 1895 |
+
"office-white-collar": 1
|
| 1896 |
+
}
|
| 1897 |
+
},
|
| 1898 |
+
{
|
| 1899 |
+
"user": "cmunley",
|
| 1900 |
+
"submission_id": "mixed-skills-v8",
|
| 1901 |
+
"run_id": "bb18cca416d850f02c05b482",
|
| 1902 |
+
"solved": 4,
|
| 1903 |
+
"steps": 21,
|
| 1904 |
+
"logged": 21,
|
| 1905 |
+
"grad_steps": 18,
|
| 1906 |
+
"grad_norm_sum": 6.835,
|
| 1907 |
+
"mean_reward": 0.4,
|
| 1908 |
+
"scores": {
|
| 1909 |
+
"industrial-physical-systems": 2,
|
| 1910 |
+
"office-white-collar": 2
|
| 1911 |
+
}
|
| 1912 |
+
},
|
| 1913 |
+
{
|
| 1914 |
+
"user": "GlobalBusinessAdvisors",
|
| 1915 |
+
"submission_id": "decision-ledger-software-d2-20261018",
|
| 1916 |
+
"run_id": "9f129154f9335c7e21f4dd6e",
|
| 1917 |
+
"solved": 3,
|
| 1918 |
+
"steps": 291,
|
| 1919 |
+
"logged": 291,
|
| 1920 |
+
"grad_steps": 189,
|
| 1921 |
+
"grad_norm_sum": 137.216,
|
| 1922 |
+
"mean_reward": 0.204,
|
| 1923 |
+
"scores": {
|
| 1924 |
+
"industrial-physical-systems": 1,
|
| 1925 |
+
"office-white-collar": 2
|
| 1926 |
+
}
|
| 1927 |
+
},
|
| 1928 |
+
{
|
| 1929 |
+
"user": "ruv",
|
| 1930 |
+
"submission_id": "metaharness-darwin-2026-10-18-redraw-36468965c9",
|
| 1931 |
+
"run_id": "130b1b1c74dfd37b074c7b6b",
|
| 1932 |
+
"solved": 3,
|
| 1933 |
+
"steps": 244,
|
| 1934 |
+
"logged": 244,
|
| 1935 |
+
"grad_steps": 122,
|
| 1936 |
+
"grad_norm_sum": 100.688,
|
| 1937 |
+
"mean_reward": 0.175,
|
| 1938 |
+
"scores": {
|
| 1939 |
+
"industrial-physical-systems": 1,
|
| 1940 |
+
"office-white-collar": 2
|
| 1941 |
+
}
|
| 1942 |
+
},
|
| 1943 |
+
{
|
| 1944 |
+
"user": "ajinkyamulay",
|
| 1945 |
+
"submission_id": "analyst-workbench-v7-r2",
|
| 1946 |
+
"run_id": "23e0988c2f5d4abff0a4d79e",
|
| 1947 |
+
"solved": 4,
|
| 1948 |
+
"steps": 94,
|
| 1949 |
+
"logged": 70,
|
| 1950 |
+
"grad_steps": 20,
|
| 1951 |
+
"grad_norm_sum": 9.008,
|
| 1952 |
+
"mean_reward": 0.063,
|
| 1953 |
+
"scores": {
|
| 1954 |
+
"finance-economics": 1,
|
| 1955 |
+
"industrial-physical-systems": 1,
|
| 1956 |
+
"office-white-collar": 2
|
| 1957 |
+
}
|
| 1958 |
+
},
|
| 1959 |
+
{
|
| 1960 |
+
"user": "dnakhla",
|
| 1961 |
+
"submission_id": "terminal-desk-v5-rd20261019",
|
| 1962 |
+
"run_id": "ade9de19553709cfe0787fa3",
|
| 1963 |
+
"solved": 1,
|
| 1964 |
+
"steps": 142,
|
| 1965 |
+
"logged": 142,
|
| 1966 |
+
"grad_steps": 93,
|
| 1967 |
+
"grad_norm_sum": 167.469,
|
| 1968 |
+
"mean_reward": 0.518,
|
| 1969 |
+
"scores": {
|
| 1970 |
+
"office-white-collar": 1
|
| 1971 |
+
}
|
| 1972 |
+
}
|
| 1973 |
+
],
|
| 1974 |
+
"missing": []
|
| 1975 |
+
}
|
analysis/2026-10-19/field-gradients.md
ADDED
|
@@ -0,0 +1,51 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
# Does the amount of real training predict the score? (2026-10-19)
|
| 2 |
+
|
| 3 |
+
Data: all 131 heldout-v2 runs on the leaderboard at 14:00 EDT (`leaderboard-2026-10-19-1400.json`), joined with each
|
| 4 |
+
run's public Trackio metrics (`scripts/field_gradients.py`, per-run summary `field-gradients-2026-10-19.json`). A step
|
| 5 |
+
changed the model if its `grad_norm` was above 0; steps where every rollout in the group scored the same have zero
|
| 6 |
+
advantage, zero loss and zero `grad_norm`. Trackio logged under 90% of the steps for 46 runs, so for those the counts
|
| 7 |
+
are lower bounds.
|
| 8 |
+
|
| 9 |
+
## Results
|
| 10 |
+
|
| 11 |
+
Score by the number of steps that changed the model (bucket edges chosen before looking at the scores):
|
| 12 |
+
|
| 13 |
+
| Updating steps | Runs | Mean solved | Distribution |
|
| 14 |
+
|---|---|---|---|
|
| 15 |
+
| 0 | 5 | 1.60 | 0:1, 1:1, 2:2, 3:1 |
|
| 16 |
+
| 1–25 | 21 | 3.14 | 2:7, 3:6, 4:6, 5:2 |
|
| 17 |
+
| 26–50 | 25 | 3.20 | 1:1, 2:2, 3:14, 4:7, 5:1 |
|
| 18 |
+
| 51–100 | 59 | 2.59 | 0:3, 1:8, 2:16, 3:19, 4:9, 5:4 |
|
| 19 |
+
| 101–150 | 14 | 3.14 | 1:1, 2:2, 3:5, 4:6 |
|
| 20 |
+
| 151+ | 7 | 2.29 | 1:1, 2:3, 3:3 |
|
| 21 |
+
|
| 22 |
+
By the share of logged steps that changed the model: 0% 1.60 (5), 1–20% 3.21 (14), 21–40% 2.80 (20), 41–60% 2.76
|
| 23 |
+
(55), 61–80% 3.00 (26), 81–100% 2.55 (11).
|
| 24 |
+
|
| 25 |
+
Across all runs the correlation between updating steps and score is about zero (Pearson -0.07, Spearman -0.06).
|
| 26 |
+
|
| 27 |
+
Split chosen after seeing the table, so exploratory:
|
| 28 |
+
|
| 29 |
+
| Subset | 0 updates | 1–50 updates | 51+ updates | 1–50 minus 51+ | One-sided permutation p |
|
| 30 |
+
|---|---|---|---|---|---|
|
| 31 |
+
| All 131 runs | 1.60 (5) | 3.17 (46) | 2.66 (80) | +0.51 | 0.008 |
|
| 32 |
+
| 85 runs with ≥90% of steps logged | 1.67 (3) | 3.22 (18) | 2.62 (64) | +0.60 | 0.031 |
|
| 33 |
+
|
| 34 |
+
Averaging within each user first (so prolific users do not dominate): 1–50 updates 3.08 over 25 users, 51+ updates 2.73
|
| 35 |
+
over 22 users.
|
| 36 |
+
|
| 37 |
+
## Reading
|
| 38 |
+
|
| 39 |
+
1. Runs that never changed the model score lowest (1.60, n=5). Training at all helps, as naklitechie found (#229).
|
| 40 |
+
2. Runs with 1–50 updating steps scored about half a point more than runs with more. That supports a weaker form of
|
| 41 |
+
light-touch: a few real updates, not none. The table is not a clean dose-response (101–150 is back at 3.14), the
|
| 42 |
+
1–50/51+ split was picked after looking, and runs from one user are not independent, so this is a hypothesis.
|
| 43 |
+
3. Our own runs fit loosely: v4 (29 updating steps) 5/40, v9 (45) 3/40, v10 (at least 31) 2/40; v1 (208) 2/40, v5 (186)
|
| 44 |
+
3/40, v6 (95) 0/40.
|
| 45 |
+
|
| 46 |
+
## Pre-registered check
|
| 47 |
+
|
| 48 |
+
To test point 2 on data it was not fitted to, the same script will be rerun on runs evaluated after this snapshot
|
| 49 |
+
(`evaluated_at` later than 2026-10-19 14:00 EDT) once at least 30 new runs exist. Prediction: runs with 1–50 updating
|
| 50 |
+
steps score higher on average than runs with 51 or more (one-sided permutation p < 0.05 counts as support; a difference of
|
| 51 |
+
0 or below counts against). Zero-update runs are reported separately.
|
analysis/2026-10-19/field_gradients.py
ADDED
|
@@ -0,0 +1,111 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
"""How much did each leaderboard run actually train? Fetches every run's public Trackio metrics and counts the steps
|
| 2 |
+
that changed the model (grad_norm > 0), then compares that with the run's held-out score.
|
| 3 |
+
|
| 4 |
+
A step changes the model only if its group of rollouts had some reward variance; steps where every rollout scored the
|
| 5 |
+
same have zero advantage, zero loss and zero grad_norm. Trackio sometimes logs fewer steps than the run made, so the
|
| 6 |
+
counts are lower bounds and the logged coverage is reported alongside.
|
| 7 |
+
|
| 8 |
+
Usage: python3 scripts/field_gradients.py <leaderboard snapshot json> [--after <epoch>] [--cache reports/field-gradients]
|
| 9 |
+
[--out x.json]
|
| 10 |
+
--after keeps only runs evaluated after that time (for the pre-registered check in reports/field-gradients-2026-10-19.md:
|
| 11 |
+
1792432800 = 2026-10-19 14:00 EDT).
|
| 12 |
+
"""
|
| 13 |
+
|
| 14 |
+
import argparse
|
| 15 |
+
import json
|
| 16 |
+
import os
|
| 17 |
+
import statistics as st
|
| 18 |
+
import time
|
| 19 |
+
import urllib.request
|
| 20 |
+
|
| 21 |
+
API = "https://openenvarena-training.hf.space/api/get_metric_values"
|
| 22 |
+
METRICS = ["grad_norm", "frac_reward_zero_std", "reward"]
|
| 23 |
+
|
| 24 |
+
|
| 25 |
+
def metric(project, run, name):
|
| 26 |
+
body = json.dumps({"project": project, "run": run, "run_id": run, "metric_name": name}).encode()
|
| 27 |
+
req = urllib.request.Request(API, data=body, headers={"Content-Type": "application/json",
|
| 28 |
+
"User-Agent": "five-desks-tracker/1.0"})
|
| 29 |
+
with urllib.request.urlopen(req, timeout=60) as r:
|
| 30 |
+
data = json.load(r)
|
| 31 |
+
rows = data.get("data", data) if isinstance(data, dict) else data
|
| 32 |
+
return {p["step"]: p["value"] for p in rows if isinstance(p, dict) and "step" in p}
|
| 33 |
+
|
| 34 |
+
|
| 35 |
+
def fetch(run, cache):
|
| 36 |
+
path = os.path.join(cache, run["run_id"] + ".json")
|
| 37 |
+
if os.path.exists(path):
|
| 38 |
+
return json.load(open(path))
|
| 39 |
+
got = {}
|
| 40 |
+
for name in METRICS:
|
| 41 |
+
try:
|
| 42 |
+
got[name] = metric(run["user"], run["run_id"], name)
|
| 43 |
+
except Exception as e: # a missing project or metric is recorded, not fatal
|
| 44 |
+
got[name] = {"error": str(e)}
|
| 45 |
+
time.sleep(0.3)
|
| 46 |
+
json.dump(got, open(path, "w"))
|
| 47 |
+
return got
|
| 48 |
+
|
| 49 |
+
|
| 50 |
+
def summarise(run, m):
|
| 51 |
+
g = m.get("grad_norm", {})
|
| 52 |
+
if not isinstance(g, dict) or "error" in g or not g:
|
| 53 |
+
return None
|
| 54 |
+
vals = list(g.values())
|
| 55 |
+
rew = [v for v in m.get("reward", {}).values()] if "error" not in m.get("reward", {}) else []
|
| 56 |
+
return {
|
| 57 |
+
"user": run["user"], "submission_id": run["submission_id"], "run_id": run["run_id"],
|
| 58 |
+
"solved": run["solved"], "steps": run.get("steps") or 0, "logged": len(vals),
|
| 59 |
+
"grad_steps": sum(1 for v in vals if v and abs(v) > 0),
|
| 60 |
+
"grad_norm_sum": round(sum(abs(v) for v in vals if v), 3),
|
| 61 |
+
"mean_reward": round(st.mean(rew), 3) if rew else None,
|
| 62 |
+
"scores": {k: v["solved"] for k, v in run["scores"].items() if v["solved"]},
|
| 63 |
+
}
|
| 64 |
+
|
| 65 |
+
|
| 66 |
+
def bucket_table(rows, key, edges, label):
|
| 67 |
+
print(f"\n{label}")
|
| 68 |
+
print(f"{'range':>12s} {'runs':>5s} {'mean':>5s} distribution")
|
| 69 |
+
for lo, hi in edges:
|
| 70 |
+
v = [r["solved"] for r in rows if lo <= key(r) <= hi]
|
| 71 |
+
if v:
|
| 72 |
+
dist = {s: v.count(s) for s in sorted(set(v))}
|
| 73 |
+
print(f"{f'{lo}-{hi}':>12s} {len(v):5d} {st.mean(v):5.2f} {dist}")
|
| 74 |
+
|
| 75 |
+
|
| 76 |
+
def main():
|
| 77 |
+
ap = argparse.ArgumentParser()
|
| 78 |
+
ap.add_argument("snapshot")
|
| 79 |
+
ap.add_argument("--cache", default="reports/field-gradients")
|
| 80 |
+
ap.add_argument("--out")
|
| 81 |
+
ap.add_argument("--after", type=float, default=0)
|
| 82 |
+
args = ap.parse_args()
|
| 83 |
+
os.makedirs(args.cache, exist_ok=True)
|
| 84 |
+
runs = [r for r in json.load(open(args.snapshot))["runs"] if r.get("benchmark") == "heldout-v2"
|
| 85 |
+
and (r.get("evaluated_at") or 0) > args.after]
|
| 86 |
+
rows, missing = [], []
|
| 87 |
+
for r in runs:
|
| 88 |
+
s = summarise(r, fetch(r, args.cache))
|
| 89 |
+
(rows.append(s) if s else missing.append(r["submission_id"]))
|
| 90 |
+
print(f"runs {len(runs)}, with Trackio grad_norm {len(rows)}, missing {len(missing)}")
|
| 91 |
+
if not rows:
|
| 92 |
+
return
|
| 93 |
+
cov = [r["logged"] / r["steps"] for r in rows if r["steps"]]
|
| 94 |
+
print(f"logged coverage: median {st.median(cov):.2f}, runs under 90% logged: {sum(c < 0.9 for c in cov)}")
|
| 95 |
+
bucket_table(rows, lambda r: r["grad_steps"], [(0, 0), (1, 25), (26, 50), (51, 100), (101, 150), (151, 10_000)],
|
| 96 |
+
"score by steps that changed the model (grad_norm > 0)")
|
| 97 |
+
bucket_table(rows, lambda r: round(100 * r["grad_steps"] / r["logged"]) if r["logged"] else 0,
|
| 98 |
+
[(0, 0), (1, 20), (21, 40), (41, 60), (61, 80), (81, 100)],
|
| 99 |
+
"score by percent of logged steps that changed the model")
|
| 100 |
+
xs = [r["grad_steps"] for r in rows]
|
| 101 |
+
ys = [r["solved"] for r in rows]
|
| 102 |
+
if len(set(xs)) > 1:
|
| 103 |
+
print(f"\nPearson r(grad_steps, solved) = {st.correlation(xs, ys):.3f} over {len(rows)} runs")
|
| 104 |
+
ranks = lambda a: [sorted(a).index(x) + (a.count(x) - 1) / 2 for x in a]
|
| 105 |
+
print(f"Spearman rho = {st.correlation(ranks(xs), ranks(ys)):.3f}")
|
| 106 |
+
if args.out:
|
| 107 |
+
json.dump({"rows": rows, "missing": missing}, open(args.out, "w"), indent=1)
|
| 108 |
+
|
| 109 |
+
|
| 110 |
+
if __name__ == "__main__":
|
| 111 |
+
main()
|