fix(train): fix regression from #971 - keep Muon-excluded matrices at Muon's LR

[WindChimeRan]

Purpose

Fixes a regression introduced by #971.

• See the DSpark x Markov head report: https://claude.ai/code/artifact/124e424c-9db1-42bd-a724-7d5657f59df7
• See the DFlash2 codebook report: https://claude.ai/code/artifact/380c43b0-5609-4676-bc2f-68c338b2def5

#971 correctly routed DSpark's Markov factors (markov_w1, markov_w2) out of Muon — they are vocabulary-indexed tables, and orthogonalizing one is meaningless. But build_optimizers has a single AdamW group, at lr. So the factors silently dropped from muon_lr / muon_weight_decay to lr / weight_decay — 10x lower on both, given muon_lr = 10 * lr by default. Nothing in the diff said so.

The head then barely trains. Over 10 epochs its logit bias moves from 0.006 to 0.027, against 0.587 before #971 — it isn't learning something worse, it isn't learning.

Change

The 2D matrices excluded from Muon for semantic reasons share a single AdamW optimizer with the base parameters, using a dedicated parameter group at muon_lr / muon_weight_decay.

This covers markov_w1 / markov_w2 and the DFlash2 codebooks. embed_tokens / lm_head are frozen in every speculator type, so no other model changes. Everything else from #971 is untouched — the std=0.01 init stays, and both factors stay excluded from Muon.

Results

Qwen3-8B, tutorial_regen (4993 rows), 6xH100, 10 epochs, single seed. Peak validation acceptance length:

	peak val EAL	Δ
pre-#971	2.552	—
main (#971)	2.497	−0.055
this PR	2.552	recovered

main is below pre-#971 at all ten epochs, by 0.049 to 0.072.

Test

test_vocab_factors_keep_muon_lr_not_the_base_lr fails on main with assert 0.0003 == 0.003 and passes here. Partitioning tests also cover the DFlash2 transition codebooks and verify that generic embeddings, LM heads, and unrelated codebooks retain base AdamW hyperparameters. Full unit suite shows no new failures.

Note on resuming older checkpoints

The AdamW parameter-group layout changes, so checkpoints written before this PR are not guaranteed to resume. This is deliberate: those checkpoints come from runs whose Markov head was inert, and any incompatibility fails loudly rather than silently mis-mapping optimizer state.

Fresh runs and --optimizer adamw are unaffected.

Checklist

• The purpose of the PR, such as "Fix some issue (link existing issues this PR will resolve)".
• The test plan/results, such as providing test command and pasting the results.
• (Optional) The necessary documentation update.
• I (a human) have written or reviewed the code in this pr to the best of my ability.

[coderabbitai]

📝 Walkthrough

Walkthrough

Muon optimization now separates excluded 2D matrices into a third parameter group. These matrices use Muon learning-rate and weight-decay settings through a separate AdamW optimizer. Tests verify grouping and hyperparameter selection for codebooks and Markov factors.

Changes

Muon excluded-matrix optimization

Layer / File(s)	Summary
Parameter grouping contract src/speculators/train/optimizers.py, tests/unit/models/test_dflash2_model_definitions.py, tests/unit/models/test_dspark_model_definitions.py	split_named_params_for_muon now returns muon, adamw, and excluded groups. Hint-matching matrices enter excluded, while biases and degenerate matrices remain in adamw. Model tests verify codebook and Markov-factor assignments.
Excluded-matrix optimizer construction src/speculators/train/optimizers.py, tests/unit/models/test_dspark_model_definitions.py	build_optimizers logs the excluded group and creates an AdamW optimizer with muon_lr and muon_weight_decay. Tests verify that excluded Markov factors do not use the base AdamW settings.

Merge Risk: 🔵 Low · up to 10b7a

Muon training runs with excluded matrices now use an additional optimizer, so checkpoints created before this change will not resume under the new layout and must be restarted or migrated. The failure is explicit and limited to affected recovery workflows; the PR is mergeable with owner awareness and a follow-up plan for checkpoint compatibility.

🚥 Pre-merge checks | ✅ 4 | ❌ 1

❌ Failed checks (1 warning)

Check name	Status	Explanation	Resolution
Docstring Coverage	⚠️ Warning	Docstring coverage is 50.00% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 6 functions across 3 files.	Write docstrings for the functions missing them to satisfy the coverage threshold.

✅ Passed checks (4 passed)

Check name	Status	Explanation
Linked Issues check	✅ Passed	Check skipped because no linked issues were found for this pull request.
Out of Scope Changes check	✅ Passed	Check skipped because no linked issues were found for this pull request.
Title check	✅ Passed	The title clearly and concisely identifies the regression fix: keeping Muon-excluded matrices at Muon's learning rate.
Description check	✅ Passed	The description directly explains the regression, the dedicated optimizer group, affected parameter groups, test coverage, results, and checkpoint impact.

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Actionable comments posted: 1

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Inline comments:
In `@src/speculators/train/optimizers.py`:
- Around line 123-131: Update the optimizer checkpoint restore flow around the
Muon and AdamW optimizer construction to remain compatible with checkpoints
containing only the prior two optimizers. Migrate the legacy AdamW state by
parameter name into the new optimizer layout, or explicitly reset optimizer
state while still restoring model and trainer progress; ensure checkpoint save
and resume behavior is verified.

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[fynnsu]

I don't love that this introduces a third optimizer group. Also my understanding is that the lr for muon can typically be higher because optimization should be more stable.

Obviously it's not good that the markov_w1 and markov_w2 weights aren't being trained in this example, but perhaps the learning rate in general needs to be higher (instead of specifically the lr for these weights)?

What do you think?

[WindChimeRan]

@fynnsu

I don't love that this introduces a third optimizer group.

Do you mean the code style or the algorithm correctness?

lr for muon can typically be higher because optimization should be more stable.

I agree, and it should be aligned with the batch size, you can tune config.muon_lr and config.lr according to batch size.

the learning rate in general needs to be higher (instead of specifically the lr for these weights)

I didn't specify lr for these weights, it's config.muon_lr

[WindChimeRan]

updates:

• refactoring: make the code slightly cleaner and future proof
• dflash2: extend the fix to dflash as well: ablation https://claude.ai/code/artifact/380c43b0-5609-4676-bc2f-68c338b2def5?open_in_browser=1&via=user_open&org=4d8a5f93-b754-484f-972e-39bcb25b9848