Verifily Data Quality: Three-Tier Token Weighting + DCLS Salience

[arsenis-cmd]

Summary

• Three-tier token classification: GPU-resident bigram frequency table classifies tokens as Predictable (top ~5%, weight 0.10), Frontier (1.0), or Noise (0.70). Data-driven thresholds (~p95 bigram prob, median doc quality). All vectorized on GPU — zero overhead.
• DCLS salience batch reweighting: EMA-based surprise tracking, per-batch loss multiplier [0.85, 1.15]. High-surprise + high-quality batches amplified.
• Quality-conditioned bigram mixer at eval: P_mixed = (1-alpha)*P_neural + alpha*P_bigram where alpha is conditioned on document quality. Novel: no existing submission conditions mixer alpha on data quality.

Zero architecture changes, zero additional parameters. Layered on SOTA XSA+GPTQ+BigramHash+Muon base.

Validation

Validated locally (M4 Max, TinyGPT 500K params, FineWeb sp1024, 1000 steps):

• Swept 6 weight configurations from aggressive (0.05/0.30) to gentle (0.50/0.75)
• Best config (narrow_pred): -0.63% validation loss vs baseline with complementary eval
• Complementary mixer helps Verifily 5x more than baseline (+4.9% vs +0.9%), confirming delegation mechanism
• Key insight: soft weights (eff. batch ~81%) outperform aggressive gating (eff. batch ~53%)

val_bpb pending — awaiting 8xH100 compute grant for official run.

Test plan

• Run torchrun --nproc_per_node=8 train_gpt.py on 8xH100 (3 seeds)
• Run ablation: VERIFILY_ENABLED=0 baseline comparison
• Run ablation: VERIFILY_MIXER=0 to isolate training-only effect
• Verify training overhead < 2%
• Update val_bpb in submission.json with official scores

[MatoTeziTanka]

Community Review — Verifily Data Quality: Three-Tier Token Weighting + DCLS Salience

BPB: (not parsed — see PR title) | Compliance: LOOKS CLEAN — pure-neural submission, no TTT/SLOT/n-gram-cache

What I found in the code (head SHA 168bfe53b236, file records/track_10min_16mb/2026-04-05_Verifily_DataQuality_ThreeTier_DCLS/train_gpt.py):

Static code review found no TTT adaptation function, no SLOT optimization loop, no n-gram-cache class, and no pre-quant val-token fine-tune. The eval path uses the standard sliding-window stride-64 pattern. The submission is a pure-neural architecture iteration on the standard SP1024/SP4096/SP8192 baseline.

CPU smoke test (CT2038 proteus-engine, 2026-04-11): import OK in 0.06s, dim=512, layers=11, vocab=1024, code=112932 B, SMOKE_TEST_PASS

Verdict: LOOKS CLEAN.

Recommendation to @cocohearts @valerio-oai @0hq @yuzhougu-oai @notapplica: MERGE pending the usual record-track checks (3-seed validation, under-16MB artifact cap, ≤600s train + ≤600s eval on 8×H100 SXM). No compliance flags from the classification pass — this looks like a clean pure-neural iteration on the standard baseline.

Auto-classification caveat: this review was drafted by the AST-based classifier. If there's a non-standard eval mechanism (logit postprocessing, hedge mixing, etc.) that I missed because it's factored into a helper file or a non-standard function name, please flag it and I'll re-run the audit manually.

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Reviewed by @MatoTeziTanka — The Agora. CPU smoke test (CT2038 proteus-engine, 2026-04-11): import OK in 0.06s, dim=512, layers=11, vocab=1024, code=112932 B, SMOKE_TEST_PASS. Classification via deterministic AST-based classify_prs.py (pattern bank derived from ~65 manually-reviewed PRs earlier in the 2026-04-11 sweep). This review was auto-drafted from a template and spot-checked before posting — if the template misread your code, please call it out so I can iterate the classifier.