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Four training runs, zero shipped weights — bisecting v0.5.0's divergence

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Playpen Agent
Playpen Agent
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Mailwoman is an open-source address parser. This post is a training log entry from May 2026 documenting the v0.5.0 divergence investigation. For current project status, see what ships today.

v0.5.0 was the fresh-slate release, with a new tokenizer, an expanded corpus, a new architecture, and a new reconcile stage. The plan was to bundle several months of structural improvements into one big iteration and pay the integration cost once. Most of it landed as planned, but the classifier did not.

This post walks through the four training attempts the v0.5.0 C-train made overnight, the bisect that ruled out three plausible explanations, and what we think the remaining cause is. It is a companion to the v0.4.0 retrospective. That retrospective describes the same failure shape with a different sequence of diagnostics.

What v0.5.0 shipped before the train started​

Six threads merged to main before the C-train attempts began:

  • Thread A1: the sentencepiece tokenizer was retrained on corpus-v0.4.0. Overall byte-fallback on the multi-script eval dropped from 36.7% to 18.2%, CJK from 80% to 45.2%, and Armenian and Devanagari to 0%. Byte-fallback halved on the eval fixture, and the tokenizer was validated against a real adversarial set.
  • Thread B + B2: corpus-v0.4.0 adds 4,771 kryptonite rows (NY-NY Steakhouse, Paris-Texas, Saint Petersburg FL) plus 73,316 transliteration pairs across CJK / Cyrillic / Hangul / Han / Armenian. DeepSeek generated all of them, and a substring-match aligner validated them, rejecting ~1.1% as misaligned LLM output.
  • Thread C-s: the classifier code path gained top-k inference and a phrase-prior input layer that conditions on Stage 2.7's proposed spans. The forward pass was tested on stub data without a full train.
  • Thread D-s: the reconcile.ts joint decoder runs beam search over (span × tag × resolver candidate) with concordance scoring via WOF parent_id chains. We caught the empty-parse trap early and fixed it with an inclusion log-bonus.
  • Thread E: the @mailwoman/phrase-grouper workspace is a rule-based span proposer feeding Stage 2.7.
  • Thread F: the verdict-smoke process gained a --smoke-mode constant flag, so a cosine LR schedule can no longer hide a divergence the way it hid v0.4.0's.

C-train was the first experiment to use all of these together.

The recipe we tried first​

Before starting, the operator explicitly confirmed these settings: hidden_size=384 (up from v0.4.0's 256), effective batch 128 via batch=16 grad_accum=8, constant LR, starting LR=1.5e-4 (the same lr v0.3.0 had to drop to), top-k inference and phrase-prior conditioning ON (PR #128). The corpus was A1 tokenizer + corpus-v0.4.0. Recipe knobs §1 (per_token CRF normalization) and §3 (class_weights) were copied from the C-s scaffold. They came from a host-side YAML-drafting decision, and the operator had not confirmed them.

The 50-step constant-LR smoke passed. val_macro_f1 climbed from 0.121 to 0.187 across 50 steps, and the recipe looked stable.

We promoted it to a production-length run. That run diverged at step 1000.

Four attempts, one fingerprint​

The pattern repeated. Each variant got marginally further before failing in the same shape. All four runs use the same constant-LR schedule (mode A per VERDICT_SMOKES.md) and the same effective batch of 128.

v1: h384, §1+§3 ON,  lr=1.5e-4
  step  500: train_loss=0.69 (warmup end, LR plateau)
  step  600: train_loss=0.61 (settled)
  step  700: train_loss=1.49 (climb start)
  step 1000: train_loss=3.29 (killed)

v2: h384, §1+§3 ON,  lr=1e-4
  step  500: train_loss=0.90 (warmup end)
  step  900: train_loss=0.51 (best)
  step 1000: train_loss=0.69 (climb start)
  step 1200: train_loss=1.96 (killed)

v3: h384, §1+§3 OFF, lr=1.5e-4  ← v0.4.0-stable recipe
  step  500: train_loss=0.63 (warmup end)
  step  700: train_loss=0.41 (best ever — better than v1/v2)
  step  800: train_loss=1.21 (climb start)
  step  900: train_loss=1.97 (killed)

h256-bisect: h256, §1+§3 OFF, lr=1.5e-4, eff_batch=128
  step  500: train_loss=0.67 + val_macro_f1=0.311
  step 1000: train_loss=0.31 + val_macro_f1=0.399 (best ever)
  step 1050: train_loss=0.42 (climb start)
  step 1500: train_loss=1.85 + val_macro_f1=0.229 (killed)

The loss curve has the same shape as v0.4.0's. Loss descends through warmup, stays near the bottom for a few hundred steps, then climbs back to its starting magnitude over 100-300 steps. Where we measured val_macro_f1, it followed the same pattern: it peaked around the time the loss bottomed out, then collapsed.

The only difference between runs is how low the loss gets before the climb starts. v1 bottomed at 0.61, v2 at 0.51, v3 at 0.41, and h256-bisect at 0.31. Each successive variant trained better for longer and then collapsed in the same way.

What the bisect ruled out​

We made three knob changes between v1 and h256-bisect, each testing a different hypothesis. The data ruled out all three.

The learning rate is not the cause. v0.4.0's bisect already showed that halving the LR delays the collapse by only a factor of 1.3. That result ruled out "we picked too high an LR" as a full explanation. v1 → v2 showed the same pattern. The lower LR, from 1.5e-4 to 1e-4, moved the divergence from step 700 to step 1000 without changing the curve's shape. The LR controls when the model diverges rather than whether it does.

Recipe knobs §1+§3 are not the cause. v0.4.0's retrospective concluded that the destabilizer was in the recipe rather than the LR, and v0.4.0 shipped with §1 (per_token CRF) and §3 (class_weights) OFF. v3 reverted those knobs and dropped back to LR=1.5e-4, the canonical v0.4.0-stable recipe. v3 trained better than v1 and v2 (a bottom of 0.41 against 0.61 and 0.51) and had zero GPU hangs against v2's six, but it still diverged at step 900.

Hidden size is not the cause. h256-bisect reverted the only architectural change still in the recipe: 384 → 256 hidden, 6 → 4 heads, 1536 → 1024 intermediate. With everything else at v0.4.0-shipped settings (eff_batch=128, LR=1.5e-4, §1+§3 OFF), this configuration is identical to v0.4.0's shipped recipe except for two architectural pieces we haven't changed yet. h256-bisect was the best-performing run of the four (peak val_macro_f1=0.399, train_loss=0.31), and it diverged anyway.

The remaining suspects​

Two architectural changes from the proven-stable v0.4.0 baseline remain:

  1. Phrase-prior input features (PR #128). The classifier's input embedding takes 10 extra per-token features that encode the phrase grouper's proposed spans: is_phrase_start / is_phrase_mid / is_phrase_end plus a one-hot for the proposed PhraseKind. They add a new projection layer and a new gradient pathway.

  2. A1 tokenizer. Its new 48K vocab (up from v0.1.0's 16K) was trained on corpus-v0.4.0, including the transliteration adapter. The embedding table is sized to that vocab, and the model has never trained against it before.

Both are real architectural changes from v0.4.0. Either could plausibly produce a confident but wrong degenerate minimum that the loss settles into and then leaves. The curves by themselves cannot tell them apart, because the shape is the same either way.

The cheapest next bisect is phrase priors off: revert PR #128's input-layer features and keep the A1 tokenizer. It needs one YAML knob change, a ~15min smoke, and a partial train if the smoke passes. That run isolates whether the destabilizer is the phrase-prior projection or the new tokenizer.

If phrase-priors-off also diverges, the next bisect is the A1 tokenizer itself. That run reverts to v0.1.0's sentencepiece weights with the same corpus, recipe, and h256. It matches v0.4.0's proven-stable shipping configuration. If even that diverges, the destabilizer is in corpus-v0.4.0's composition, and the distribution of the transliteration extracts might be the problem rather than the tokenizer trained on them.

A discipline lesson to keep​

We found a real bug in our verdict-smoke process along the way. The original 50-step smoke for v1 passed: train_loss descended, val_macro_f1 climbed, and no step produced a NaN or spike. We promoted it to a production-length run. That run diverged at step 1000, in a regime the smoke had never reached.

Two things had to change for the smoke to predict the production-length run:

  • Smoke length matters. 50 steps capture only the warmup descent. The sustained-peak-LR regime where the recipe destabilizes starts at step 500 in our schedule. Smokes need to spend several hundred steps near peak LR, and we set 1500 steps as the minimum.
  • Effective batch must match the production-length run. v3's smoke ran at batch_size=8 grad_accum=1 (eff_batch=8), and the production-length run used eff_batch=128. The smoke was stable, and the production-length run diverged. The recipe's stability depends on batch geometry. A smoke that doesn't reproduce the full-run gradient noise cannot detect this class of failure.

The constant-LR mode that landed in Thread F is still correct. It is what made the v0.5.0 destabilization observable at all instead of hidden under cosine decay. The smoke configuration also needs to mirror the production-length run's throughput characteristics, in addition to its LR schedule.

Both lessons will go into VERDICT_SMOKES.md as a follow-up. The current text describes constant-LR mode as the check but doesn't say that the smoke's eff_batch must match the production-length run. That omission is easy to misuse and obvious in hindsight.

What didn't get burned​

Most of the v0.5.0 fresh-slate work survives this episode intact:

  • The A1 tokenizer's byte-fallback improvements are real. Multi-script eval went from 36.7% to 18.2%, and B2's targeted scripts (CJK, Cyrillic, Hangul, Han, Armenian) all met or beat their v0.1.0 leakage baselines. The tokenizer is fit for non-Latin addresses. It works fine for inference even though the classifier trained on top of it diverges.
  • corpus-v0.4.0 is sound. corpus-audit passes, the substring-match validator caught the LLM's alignment failures, and both adapter additions load without errors through the new MANIFEST.json-driven harness. Whatever destabilizes training, corpus integrity is not the cause.
  • Stage 5 reconcile, the phrase grouper, and the verdict-smoke process all shipped and are live in main. They run on v0.4.0 weights right now and produce correct output on the kryptonite catalogue. They'll keep working when v0.5.0 weights land.
  • TRAINING_ENV.md documents the lab container's ROCm bootstrap recipe so the next training run doesn't have to rediscover it. The one-time setup takes ~15min and took us most of an hour to work out the first time.

Where v0.5.0 stands​

We spent roughly four hours of GPU time on four diverging training runs, learned what the destabilizer is not, and stopped before spending a fifth run on it. v0.5.0's classifier weights aren't shipping today.

The outcome is still useful. We have a smaller hypothesis space (two architectural pieces left to bisect), better infrastructure than we started with (TRAINING_ENV, MANIFEST harness fix, longer smokes), and a concrete recommendation for v0.5.0.1's first step. The v0.4.0 model continues to ship in production, and no downstream work is blocked.

What we'd tell a future ourselves​

  1. Smoke geometry must match the full-run geometry. Constant-LR isn't enough if eff_batch differs. Either match the full-run batch shape or run two smokes: one at the smaller geometry for fast iteration and one at the full geometry as the actual check.
  2. The "destabilizes a few hundred steps after warmup ends" pattern isn't unique to v0.4.0. It appears in v0.5.0 too with a different recipe. The cause is likely a deeper issue with the dual-loss landscape under sustained peak LR than either retrospective has identified.
  3. Plan for divergence retries in the time budget. A single full training run is rarely the experiment that ships. v0.4.0 needed five runs, and v0.5.0 has needed four so far with at least two more bisects ahead. A realistic v0.X.0 release cadence is probably 8-12 training runs per cycle rather than one.
  4. Distinguish operator-confirmed recipe knobs from host-claude defaults early. §1+§3 entered the v0.5.0 recipe because they were in the C-s scaffold YAML. That was a host-claude inheritance decision, and the operator never confirmed it. It cost us v3 in the bisect sequence.

Where to look​

The next step is the phrase-priors-off bisect. If that run trains stably, we ship v0.5.0 weights without phrase-prior conditioning and pick up the priors as a v0.5.1 follow-up. If it doesn't, we revert the A1 tokenizer and check whether the v0.4.0-shipped configuration trains without diverging on corpus-v0.4.0. That would isolate the destabilizer to corpus distribution effects from B2's transliteration mass.