docs(qwen3.6): add data-format ablations and move SciCode diagnosis to ABLATIONS.md

[kevalmorabia97]

What does this PR do?

Type of change: documentation

Adds the Qwen3.6-35B-A3B QAD data-format experiments to the tutorial and moves non-recipe experiments out of the main README into a new ABLATIONS.md, following the Nemotron-3-Nano tutorial's layout.

• New ABLATIONS.md:
  • Data format. Compares pre-tokenized blend (loss on all tokens) vs chat SFT data (assistant-only loss) vs self-distilled chat SFT (BF16 teacher regenerates the responses). It covers all six benchmarks and SciCode capped rate and median length at iterations 50, 300 and 500, plus the GPU-hour cost of each format.
  • Main finding: chat SFT leads at iteration 50 (mean −0.34 vs −1.30 vs BF16) but trails by iteration 500 (−0.83 vs −0.30), with a higher SciCode runaway rate (~5.8% vs 3.6%) and ~2.9× the QAD GPU-hours (~2,150 vs ~735). Self-distillation is no better on average at step 50 and costs extra generation.
  • SciCode non-termination diagnosis. The presence_penalty per-build table, the rate-cost trade-off and the Lotfi et al. comparison are moved here verbatim from the README.
  • Stop-token probe redone. The README's earlier probe (teacher ranks <|im_end|> 5th inside loops) used no chat template and one 6K-token trace. It is replaced by a full-vocabulary probe of 16 normal and 16 runaway SciCode sub-steps with the eval's exact prompts, under the teacher and the PTQ, QAD-50 and QAD-500 students. Findings:
    • The teacher's top-4096 tokens hold ~all of its probability (1e-5 outside on average).
    • Every student stops where the teacher stops.
    • Inside the loops the teacher also keeps going (stop probability ~e^-22).
    • Two training-side follow-ups did not fix the runaways: a ghost-token top-4096 KL (SciCode capped 5.0% vs 4.3%) and one offline round of on-policy distillation from iteration 50 (5.2% vs 5.5% on teacher responses). presence_penalty stays the recommended fix.
• README: the SciCode <details> block becomes one paragraph: what it is, the presence_penalty: 1.5 fix, and when not to use it, with a link to ABLATIONS.md. The intro and the "better data blend" item link to the ablation. The recommended recipe and the main results are unchanged: pre-tokenized blend, 500 iterations.
• Tutorials index: mentions the ablations.

The chat-SFT runs used two distill.py options that are not in this repository: in-batch packing of SFT rows and a zero MoE aux-loss coefficient. They are not upstreamed because the format did not win. ABLATIONS.md says so and lists the gotchas for anyone repeating the comparison.

Testing

• Docs only. pre-commit (markdownlint) passes.
• Checked every number against the evaluation pool:
  • accuracy pooled per recorded NEL invocation;
  • SciCode capped/median from per-response output.jsonl;
  • GPU-hours from sacct elapsed × 128 GPUs.
• Checked that the new in-page and cross-file anchors resolve to existing headings.

Before your PR is "Ready for review"

• Is this change backward compatible?: N/A
• If you copied code from any other sources or added a new PIP dependency, did you follow guidance in CONTRIBUTING.md: N/A
• Did you write any new necessary tests?: N/A
• Did you update Changelog?: N/A
• Did you get Claude approval on this PR?: Yes

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Summary by CodeRabbit

• Documentation
  • Added ablation results comparing pre-tokenized training, chat SFT, and self-distilled chat SFT, including benchmark results and training costs.
  • Documented SciCode non-termination findings and the benchmark-specific accuracy and throughput trade-offs of using a decode-time presence penalty.
  • Updated the tutorial guides with output-length findings, results showing that changing only the data format did not help, and links to detailed ablations.

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📝 Walkthrough

The Qwen3.6 tutorial adds an ablation report on training formats and SciCode non-termination. The tutorial and index now summarize or link to training comparisons, presence-penalty results, and related measurements.

Changes: Qwen3.6 ablation documentation

Layer / File(s)	Summary
Ablation report and tutorial summaries examples/megatron_bridge/tutorials/Qwen3.6-35B-A3B/ABLATIONS.md, examples/megatron_bridge/tutorials/Qwen3.6-35B-A3B/README.md, examples/megatron_bridge/tutorials/README.md	The ablation report compares pre-tokenized, chat SFT, and self-distilled chat SFT results. It records training costs, SciCode termination findings, presence-penalty results, and follow-up experiments. The Qwen3.6 tutorial summarizes selected results and links to the report. The tutorial index now lists the ablations.

Priority: ⬇️ Low

Estimated code review effort: 1 (Trivial) | ~5 minutes

Change: Other

Merge Risk: 🔵 Low · up to 424aa

The report slightly overstates the iteration-300 comparison, which could mislead readers about the training-format results. The issue is localized and straightforward to correct.

Pre-merge checks: 6

Check name	Status	Explanation
Docstring Coverage		No functions found in the changed files to evaluate docstring coverage. Skipping docstring coverage check. Docstring coverage is scoped to functions touched by this diff. Analyzed 0 functions across 0…
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Security Anti-Patterns		PASS. The pull request changes only three Markdown files under the tutorials directory. It adds no Python code, dependency files, or security-sensitive calls. The added lines contain no `torch.load(..…
Description Check		Check skipped - CodeRabbit’s high-level summary is enabled.
Title check		The title clearly and concisely describes the main changes: adding data-format ablations and moving the SciCode diagnosis to ABLATIONS.md.

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PASS. The pull request changes only three Markdown files under the tutorials directory. It adds no Python code, dependency files, or security-sensitive calls. The added lines contain no torch.load(..., weights_only=False), numpy.load(..., allow_pickle=True), hardcoded trust_remote_code=True, eval(), exec(), or # nosec usage.

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@examples/megatron_bridge/tutorials/Qwen3.6-35B-A3B/ABLATIONS.md:
- Line 35: Update the iteration-50 benchmark count in the “Chat SFT's early lead
does not last” summary to say chat SFT leads on 4 of 6 benchmarks, ties on
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Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 69.09%. Comparing base (54b684b) to head (aac152a).
⚠️ Report is 15 commits behind head on main.

Additional details and impacted files

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##             main    #2835      +/-   ##
==========================================
- Coverage   69.57%   69.09%   -0.49%     
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Review comments at
@examples/megatron_bridge/tutorials/Qwen3.6-35B-A3B/ABLATIONS.md:
- Line 35: Update the iteration 300 comparison in the ablation summary to say
the two formats are nearly tied rather than tied; leave the surrounding
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Claude review summary

Scope: Full review. 3 files changed (118 additions, 49 deletions), all Markdown — all three reviewed: the new ABLATIONS.md, the Qwen3.6-35B-A3B/README.md edits, and the tutorials index row. No source, config, or test files in the diff. Since this is docs-only, the review focused on what the prose claims versus what its own tables show, and on whether the things it tells the reader to run actually exist in the repo.

Findings by severity — CRITICAL: 0 · IMPORTANT: 0 · SUGGESTION: 3

All three are internal-consistency issues in the reported numbers. None blocks merge.

1. BF16 SciCode median is reported twice with different values — 1,806 in the data-format table vs 1,817 in the presence-penalty table, for the same build and same stated pooling. 1,817 is the one that reproduces the document's +91.6% headline (3,482 / 1,817 = 1.916).
2. The iteration-500 attribution names the wrong benchmark — "mostly tau2-bench and MMMU-Pro"; the per-benchmark gaps make it tau2-bench (+1.9) and SciCode (+0.7), with MMMU-Pro third at +0.5. Worth fixing because SciCode is the benchmark the rest of the document is about.
3. README compresses "accuracy neutral across four builds" into "accuracy unchanged" next to the single build that moved most (QAD, 40.2 to 39.6). ABLATIONS.md scopes this claim carefully and explicitly declines to attribute that row's drop; the README summary drops the scoping.

Also still open: CodeRabbit's existing finding on line 35 ("5 of 6" should be 4 ahead / 1 tie / 1 behind at iteration 50) — confirmed correct against the table; I did not duplicate it as a separate comment.

What I verified and found correct. The quantitative backbone of this document holds up well:

• Every "Mean delta" cell recomputes exactly from its column (all eight columns, to the stated rounding).
• Cost arithmetic: 735 GPU-h / 128 = 5.7 h, 2,150 / 128 = 16.8 h, 2,150 / 735 = 2.9x, 14 / 4 = 3.5x micro-batches. Tokens-per-step reconciles across all three formats (~16M each), which is the right invariant for a same-tokens cost comparison.
• The fp32 logit figure: 32,768 x 248,320 x 4 B = 30.31 GiB, matching the main README.
• Rate-cost table: 1,475 to 1,192 = 1.24x, 5,109 to 1,067 = 4.8x, −55% tokens.
• Median/mean claims: "flat or slightly up on three of four builds", "mean falls 33-55%", "+71% with the penalty on", "deltas span −0.59 to +0.89", "recovers 94 sub-steps, up to +3.5 pp" — all verified.
• Capped counts: 20/2,704 = 0.7%, 96/2,704 = 3.6%, 338 x 8 = 2,704.
• Self-distillation claims (IFBench fully recovered and the only it50 build to do so; SciCode −1.7; capped rate and median matching chat SFT) all check out against the table.
• Ghost-token control (latest commit): its 4.3% baseline matches the pre-tok it50 capped rate, so it is compared against the right reference.

Repo-facing claims also check out:

• --sft, --sft_hf_dataset, --logit_kl_top_k all exist in examples/megatron_bridge/distill.py; --sft_loss_mode defaults to assistant, consistent with the "loss on assistant tokens only" description.
• The shipped eval_configs/scicode.yaml does carry the presence-penalty line commented out inside params_to_add, exactly as both files claim, with max_new_tokens: 131072 matching the quoted cap.
• The two non-upstreamed options (in-batch packing, zero MoE aux-loss coefficient) are indeed absent from distill.py — the document is correct to say the runs are not reproducible as-is, and flagging that explicitly rather than quietly omitting it is the right call.
• All new in-page and cross-file anchors resolve, including both ABLATIONS.md section slugs and the two README targets.
• No stale references left behind by the removed collapsible block; the README's earlier "see Output length below" pointer still resolves.

Assessment — risk: very low. Documentation only: no code, no API surface, no mode registration, no export path, nothing that can affect a user's checkpoint or runtime. The restructuring into ABLATIONS.md follows the Nemotron-3-Nano tutorial's established layout, and the README's recommended recipe and headline results are unchanged.

Two things worth calling out as good practice: replacing the earlier stop-token probe (one 6K trace, no chat template) with a full-vocabulary probe over 32 sub-steps using the eval's exact prompts is a real strengthening of the evidence, not just a rewording — and it correctly retracts the conclusion the weaker probe supported, redirecting the "next run" advice from top-p / full-vocab KL to on-policy distillation. Documenting a negative result at this level of detail, including the ~2.9x cost of the format that lost, is more useful to the next person than a clean recipe would be.

The three suggestions are worth a quick pass since this file's value rests on its numbers reconciling, but they are cosmetic relative to the content.

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[claude]

Claude review passed — no blocking issues found. LGTM

[cjluo-nv]

Bot review (gpt-6.1-sol) — DM the bot to share feedback.

Nudge: the ablation results are useful, but the data-format and stopping conclusions need to reflect the documented experimental limitations.

Needs action:

• Qualify README.md’s “Changing the data format alone does not help” and ABLATIONS.md’s causal conclusions: packing and teacher nondeterminism also changed. Say these chat-SFT configurations did not improve the final mean, with the comparison provisional.
• Narrow ABLATIONS.md’s “QAD does not suppress stopping” to the measured result: dequantized students preserve </think> probability on sampled teacher prefixes. Distinguish ending reasoning from emitting the actual stop token, and avoid extrapolating to W4A4 serving.

No action needed:

• The SciCode config matches the uncommenting instructions, and the new cross-file anchors resolve. No runtime code or tests change.