memory: snapshot — run 7 training on gx10; run 6 TRANSFERRED after CSAM adjudication; erp-tune-v6-nvfp4a16 live as trial; ESH/YTVC/webhook repairs; ana-ml2 routes persisted; tank/zroot actions deferred to next session. Index 830→271 lines: 27 decisions + 8 abandoned archived, superseded in-flight blocks archived verbatim

2026-09-09 00:26:44 -07:00
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-# ERP/RP tune run-01 COMPLETE — 7.36h, gate passed on the axis it was built for
-
-`[2026-08-25]`
-
-## The run
-
-    1312/1312 in 7:21:52   train_loss 2.793   epoch 1.0
-    20.1 s/it FLAT across every 100-step window   (round 1: 35-46.5 s/it)
-    adapter: /tank/erp-tune/run-01/adapter/  410 tensors, provenance.json
-
-**47% faster than the round-1 projection of 13.85h**, from two changes: the
-bucketed sampler and flex attention. Rate was flat — 19.7 / 19.8 / 20.4 / 20.3
-across the four 100-step windows — which means the 35-46.5 spread in round 1 was
-*entirely padding*, and removing padding removed the variance rather than just
-the mean.
-
-⚠ **I quoted three different ETAs (6.9h, 8h, 7.3h) before I started using a
-rolling average.** The first two were instantaneous tqdm readings off a number
-that swings 17-25 s/it with batch width. Only the rolling rate was honest. Same
-measure-don't-sample discipline I wrote into the throughput playbook, violated on
-the one metric I kept reporting.
-
-## lora_B gate — PASSED, twice
-
-    checkpoint-100   205/205 non-zero, median norm 0.829
-    final adapter    205/205 non-zero, median norm 1.708
-    vision_tower tensors: 0 on both
-
-Median norm rising 0.829 -> 1.708 means it kept learning through the whole run
-rather than saturating early. This check **never ran in round 1** (died at step
-19, first checkpoint was 100) and it is the only failure mode that stays
-invisible until the acceptance gate reports base-identical numbers.
-
-## The gate — brokkr-smithy-dev
-
-**It did the thing it was built to do:**
-
-    metric                    base A/B        tuned      delta      floor
-    attractor hit rate    94.8% / 96.8%       84.5%     -11.3pt     2.0pt
-    diversity (pairwise)  0.213 / 0.221      0.3948     +0.178     0.008
-
-Diversity moved **22x its own noise floor**. Attractor rate (how often the model
-reaches for the same names and phrasings) fell 11 points against a 2-point floor.
-
-    T1 100 · T2 95 · T3 96-97 · T4 98 · T5 100 · T6 81-82 · core ~94.2
-    memorisation: 0.0000 on BOTH arms, all three corpora
-
-**Zero memorisation closes the R20 licensed-prose exposure on measurement rather
-than argument.**
-
-⚠ **Caveat brokkr volunteered rather than buried:** the tuned arm lost 18 of 192
-generations to truncation/degeneracy against base's 1-2. Lopsided exclusions
-plausibly flatter the diversity magnitude. Direction is unambiguous at 22x floor;
-the number carries an asterisk.
-
-## The noise-floor near-miss — the methodology lesson
-
-brokkr was one step from reporting a 13-point T6 regression **that sat inside
-twice his instrument's own variance.**
-
-    --per-type 32     max swing across tasks:  9 points
-    --per-type 128    max swing across tasks:  1 point
-
-His gate criterion is "no task regresses by more than one item" = 3.1 points at
-n=32. **The instrument's own run-to-run noise was 3 items.** He was scoring a
-preregistered gate at 4x finer resolution than it could resolve, and caught it by
-running a control he did not strictly need. Quadrupling n collapsed the noise
-exactly as binomial statistics predicts.
-
-⚠ **Root cause of the noise is a property of the SEAT:** `max-num-seqs` is unset,
-so with a 218,625-token KV cache the scheduler batches freely up to vLLM's
-default of 256. Continuous batching changes reduction order and borderline items
-flip. Temperature 0 buys deterministic *sampling*, not deterministic
-*arithmetic*. He declined a `--max-num-seqs 1` determinism control for the right
-reason: a floor measured on a seat serving one request at a time is not the floor
-that applies to the seat we ship.
-
-## The confound I built and he caught
-
-I optimised a pipeline for production and then handed him its output as an eval
-instrument **without asking whether those were the same job.** The tuned arm
-would have reached the seat as NVFP4A16 while his base arm was bf16 — any
-regression would have been tuning-damage OR quantization-damage with no way to
-separate them, and the gate's whole question is "did the tune cost us
-capability."
-
-**Both arms now bf16, same seat, same port, argv differing in exactly two
-lines** (weights path, served name), template sha256 identical
-(`ae53464bf3be2580`), KV cache identical to the digit (218,625 tokens across all
-three launches). Quantization moved *downstream* of the gate.
-
-See [[2026-08-25-refusal-retention-probe]] for the axis his gate did not have.