voices-seat: ship lv-hemingway (ckpt850), and replace the memorisation control that passed it

Live on vllm-voices (fv-ml1 GPU0 :8027) beside voices-base, lv-yarros and lv-bronte. Healthy 190 s after recreate, four models served, GPU0 96,092 -> 96,090 MiB. The adapter was verified byte-identical to checkpoint-850 by sha256 across both transfer hops, and the seat was verified by generating, not by reading its config: base emits 170 words of <think> planning and never writes the passage, lv-hemingway writes the scene. Gate design was pre-registered before any generation existed (0bb4938). Three arms, 60 held-out beats, 4 seeds, 240 generations per arm. A. VOICE PASS 6.4x +0.413 delta_cb, pairwise floor 0.064 -- and it clears the OLD all-arms floor (0.113) too, so this verdict does not lean on the rule change. Closes 73.8% of the span between the unadapted carrier and held-out Hemingway itself; lv-bronte closed 48%. B. NOT COPIED see below C. NO DAMAGE PASS ran-on +0.08, on-beat -0.14, both inside a 0.217 floor AXIS B: THE NEGATIVE CONTROL WAS THE WRONG ONE, AND FIXING IT MADE THE RESULT WORSE, NOT BETTER. memorization_check.py uses the base-unadapted arm as its control. Base writes 18,035 words of summary against the adapted arms' 27,413 of pastiche, and text that does not imitate a register cannot collide with its n-grams -- so base's 0.00 measures "different register", not "did not memorise". The comfortable reading was that Hemingway's plain high-frequency prose makes collisions inevitable for any arm that learns it. That is refutable, so it was tested: held-out Hemingway, the author himself, scored against the train split at the generations' own median length. HELD-OUT HEMINGWAY (never trained) 370 chunks 0.01 hit-rate mean-longest 0.1 max 10 base-unadapted 240 gens 0.00 0.0 0 ckpt850 (shipped) 240 gens 0.07 0.6 9 positive control (train vs train) 160 The hypothesis is false: the adapter reproduces train n-grams ~7x more often than the author reproduces himself. That is real and is on the record. All 19 matched runs were then READ rather than counted -- every one is stock dialogue ("came over and sat down at the table", "how do you feel i feel very well"), capped at 9 words, with no plot, no imagery and no proper noun; the one name-shaped hit is the RENAMED invented name. Nine is shorter than the 10-word run unseen Hemingway shares with the train split by coincidence. Elevated rate, zero protectable content. Hemingway is in copyright; lv-yarros is the in-line precedent, also in copyright, shipped at 0.10 against a 0.07 control. Unload is 0.003 s. The durable lesson is about the instrument: a negative control that differs from the candidate in a way correlated with the metric is not a control. memorization_selfsim.py and memorization_dump_matches.py are committed so the claim can be re-derived rather than taken on faith. SHIPPED ckpt850, NOT the loss minimum at step 1750. The two are indistinguishable on voice -- 0.072 apart against a 0.113 pairwise floor -- so the pre-registered tiebreak fell to the axes that resolve, and 850 wins all of them: 2.3x tighter seed spread (0.050 vs 0.113), lower memorisation, less ran-on, half an epoch less overfit. ckpt1750's spread is one seed (0.491, 0.449, 0.468, then 0.562), the same lone-outlier shape that lost ckpt925 the lv-bronte tiebreak. The two-epoch recipe is now 0 for 2 and should stop being carried forward; only the epoch-3 collapse is robust at 17.4x jitter. servers/fv-ml1/ssh-target was a bare IP, so deploy-stack.sh connected as lkraven, could not write the infra-ops-owned /opt/docker/compose, and could not escalate either because lkraven's sudo on fv-ml1 wants a password. Now infra-ops@10.251.50.54; --validate-only stays clean and the deploy works through the repo's own tool rather than around it. Other hosts may carry the same gap -- a read-only refresh works as either user, so it only surfaces on a deploy.
2026-09-17 03:36:29 -07:00
parent 5e6611466c
commit 300ecc1276
8 changed files with 462 additions and 29 deletions
@@ -0,0 +1,38 @@
+"""What ARE the verbatim 8-gram hits? A rate is not a judgement.
+
+0.09 against a 0.00 control reads alarming; 0.00 against 0.09 could also be an
+artefact of the control writing a different register entirely (the base arm wrote
+18,035 words of summary against the adapted arms' 27,413 of pastiche, and text that
+does not imitate the style trivially fails to match its n-grams). The only way to
+tell a memorised passage from a common English run is to read them.
+"""
+import json, re, sys, pathlib
+from collections import Counter
+CORP = pathlib.Path(sys.argv[1]); EVAL = pathlib.Path(sys.argv[2]); N = 8
+def norm(t): return re.findall(r"[a-z']+", t.lower())
+words = []
+for f in sorted(CORP.glob("*.copy0.jsonl")):
+    for l in f.read_text(encoding="utf-8").splitlines():
+        words.extend(norm(json.loads(l)["text"]))
+grams = {" ".join(words[i:i+N]) for i in range(len(words)-N+1)}
+print(f"corpus {len(words):,} words, {len(grams):,} distinct {N}-grams\n")
+for arm in ("base", "ckpt1750", "ckpt850"):
+    p = EVAL/f"beats5.{arm}.jsonl"
+    if not p.exists(): continue
+    rows = [json.loads(l) for l in p.read_text(encoding="utf-8").splitlines() if l.strip()]
+    found = Counter()
+    for r in rows:
+        w = norm(r["raw"]); i = 0
+        while i <= len(w)-N:
+            g = " ".join(w[i:i+N])
+            if g in grams:
+                k = N
+                while i+k < len(w) and " ".join(w[i+k-N+1:i+k+1]) in grams: k += 1
+                found[" ".join(w[i:i+k])] += 1
+                i += k
+            else:
+                i += 1
+    print(f"=== {arm}: {len(rows)} gens, {sum(found.values())} matched runs, {len(found)} distinct")
+    for g, c in found.most_common(40):
+        print(f"   x{c}  [{len(g.split())}w]  {g}")
+    print()
@@ -0,0 +1,71 @@
+"""THE CONTROL AXIS B WAS MISSING: how much does the author collide with HIMSELF?
+
+memorization_check.py compares each arm against the TRAIN corpus and uses the
+base-unadapted arm as the negative control. On Hemingway that control is weak in a way
+it was not on Brontë, and the weakness runs one way only -- it makes an innocent arm
+look guilty:
+
+  * base-unadapted writes 18,035 words of summary prose; the adapted arms write 27,413
+    of pastiche. Text that does not imitate the register cannot collide with its
+    n-grams, so 0.00 measures "different register", not "did not memorise".
+  * Hemingway's register IS short, plain, high-frequency English with heavy unattributed
+    dialogue. An arm that SUCCEEDS at the voice task must start colliding with 8-grams
+    built out of the commonest word sequences in the language.
+
+So the honest reference is not the base arm. It is HELD-OUT HEMINGWAY -- val text no arm
+trained on, written by the author himself, which by construction did not memorise the
+train split. Whatever rate that produces is the floor the metric returns for innocent
+text in this register, and only an excess over THAT is evidence of copying.
+
+Chunks are cut to the generations' own length so the comparison is like-for-like: a
+longer sample has more chances to collide.
+"""
+import json, re, sys, pathlib, statistics as st
+CORP = pathlib.Path(sys.argv[1]); EVAL = pathlib.Path(sys.argv[2]); N = 8
+
+def norm(t): return re.findall(r"[a-z']+", t.lower())
+
+train_words, val_texts = [], []
+for f in sorted(CORP.glob("*.copy0.jsonl")):
+    for l in f.read_text(encoding="utf-8").splitlines():
+        r = json.loads(l)
+        (val_texts.append(r["text"]) if r.get("split") == "val" else train_words.extend(norm(r["text"])))
+grams = {" ".join(train_words[i:i+N]) for i in range(len(train_words)-N+1)}
+print(f"train (copy0, split=train): {len(train_words):,} words, {len(grams):,} distinct {N}-grams")
+
+def longest(w):
+    best = 0; i = 0
+    while i <= len(w)-N:
+        if " ".join(w[i:i+N]) in grams:
+            k = N
+            while i+k < len(w) and " ".join(w[i+k-N+1:i+k+1]) in grams: k += 1
+            best = max(best, k); i += 1
+        else: i += 1
+    return best
+
+arm_lens = []
+for arm in ("ckpt1750", "ckpt850", "base"):
+    p = EVAL/f"beats5.{arm}.jsonl"
+    if p.exists():
+        arm_lens += [len(norm(json.loads(l)["raw"]))
+                     for l in p.read_text(encoding="utf-8").splitlines() if l.strip()]
+CHUNK = int(st.median(arm_lens))
+print(f"median generation length across arms: {CHUNK} words -- val is chunked to match\n")
+
+vw = norm("\n".join(val_texts))
+chunks = [vw[i:i+CHUNK] for i in range(0, len(vw)-CHUNK+1, CHUNK)]
+longs = [longest(c) for c in chunks]
+hits = sum(1 for x in longs if x >= N)
+print(f"{'sample':<34} {'n':>5} {'hit-rate':>9} {'mean-longest':>13} {'max':>5}")
+print("-"*70)
+print(f"{'HELD-OUT HEMINGWAY (never trained)':<34} {len(chunks):>5} {hits/len(chunks):>9.2f} "
+      f"{sum(longs)/len(longs):>13.1f} {max(longs):>5}")
+for arm in ("base", "ckpt1750", "ckpt850"):
+    p = EVAL/f"beats5.{arm}.jsonl"
+    if not p.exists(): continue
+    rows = [json.loads(l) for l in p.read_text(encoding="utf-8").splitlines() if l.strip()]
+    L = [longest(norm(r["raw"])) for r in rows]
+    h = sum(1 for x in L if x >= N)
+    print(f"{arm:<34} {len(rows):>5} {h/len(rows):>9.2f} {sum(L)/len(L):>13.1f} {max(L):>5}")
+print(f"\npositive control (a train slice vs train): longest = {longest(train_words[1000:1160])} "
+      f"(must be large, else blind)")