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

records/track_10min_16mb/2026-04-05_Verifily_DataQuality_ThreeTier_DCLS/README.md

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+# Verifily Data Quality for Parameter Golf
+
+## Approach
+
+We layer **three data-quality components** on top of the current SOTA architecture
+(XSA-all + GPTQ + BigramHash + Parallel Muon). All modifications are in the
+loss computation and eval pipeline — zero architecture changes, zero additional
+parameters.
+
+### 1. Three-Tier Token Classification (Training)
+
+Not all tokens deserve equal gradient. We classify each token into three tiers
+using a GPU-resident bigram frequency table (4MB) built incrementally from
+training data:
+
+| Tier | Condition | Weight | Rationale |
+|------|-----------|--------|-----------|
+| **Predictable** | P_bigram > ~p95 | 0.10 | Bigram handles these; soft nudge to free neural capacity |
+| **Frontier** | P_bigram low + high quality doc | 1.0 | Learnable, high-value tokens |
+| **Noise** | P_bigram low + low quality doc | 0.70 | Lower quality, gentle gradient reduction |
+
+Weights validated locally on M4 Max: soft weights (0.10/0.70, eff. batch ~81%) beat
+aggressive weights (0.05/0.30, eff. batch ~53%) — the model needs most of its gradient
+signal intact. The threshold is data-driven (~p95 of bigram probability distribution).
+
+Document quality is scored per-batch using two GPU-vectorized signals:
+- **Vocabulary richness**: unique tokens / total (scatter-based, O(1) per token)
+- **Repetition**: fraction of tokens matching 4 positions back
+
+### 2. DCLS Salience Batch Reweighting (Training)
+
+Adapted from Verifily's [DCLS](https://github.com/verifily/dcls) episodic memory
+salience ranking. Per-batch loss multiplier in [0.85, 1.15]:
+
+```
+salience = 0.4 * surprise + 0.3 * doc_quality + 0.3 * 0.5
+multiplier = 0.85 + 0.30 * salience
+```
+
+Where `surprise = |batch_loss - EMA_loss| / EMA_loss`. High-surprise, high-quality
+batches get amplified; low-surprise, low-quality batches get dampened.
+
+### 3. Quality-Conditioned Bigram Mixer (Eval)
+
+At eval time, we mix neural predictions with bigram statistics:
+
+```
+P_mixed = (1 - alpha) * P_neural + alpha * P_bigram
+```
+
+Where alpha is **conditioned on document quality**:
+- High quality (>0.6): alpha_base = 0.15 (trust neural more)
+- Low quality: alpha_base = 0.30 (trust bigram more)
+- Scaled by bigram confidence: alpha = alpha_base * min(P_bigram * 3, 1)
+
+This is novel: no existing submission conditions the mixer alpha on data quality.
+
+## Performance Characteristics
+
+- **Training overhead**: ~0% — all operations are GPU-vectorized tensor ops
+  - BigramStats: 2x `index_put_` (update) + 2x index (lookup) per step
+  - DocumentQualityScorer: 1x `scatter_` + 1x comparison per step
+  - SalienceTracker: 3 float operations per step
+  - Three-tier weights: 2x comparison + 2x masked fill per step
+- **Memory overhead**: 8MB (4MB bigram counts + 4MB bigram totals)
+- **Eval overhead**: ~2% — one extra softmax + bigram lookup per eval batch
+
+## Ablation Plan
+
+Each component can be independently disabled via environment variables:
+
+```bash
+VERIFILY_ENABLED=0        # Disable all Verifily components
+VERIFILY_SALIENCE=0       # Disable salience tracker only
+VERIFILY_MIXER=0          # Disable eval-time bigram mixer only
+VERIFILY_NGRAM_WARMUP=0   # Enable token weighting from step 0
+VERIFILY_NGRAM_THRESH=0.185 # Bigram probability threshold for Tier 1 (~p95)
+VERIFILY_QUALITY_THRESH=0.735 # Document quality threshold for Tier 3 (~median)
+VERIFILY_PRED_WEIGHT=0.10  # Predictable tier weight (validated: soft nudge)
+VERIFILY_NOISE_WEIGHT=0.70 # Noise tier weight (validated: gentle penalty)
+```
+
+## Base Architecture (Unchanged)
+
+- 11 layers, 512d, 8 heads, 4 KV heads, 3x MLP, seq_len 2048
+- XSA (Exclusive Self-Attention) on all layers
+- BigramHash(3072, 112) + ValueEmbedding layers 9,10
+- Parallel Muon optimizer with batched Newton-Schulz
+- Full GPTQ with autoregressive self-generated calibration
+- EMA (0.997) + SWA + LZMA compression
+- Sliding window eval (stride 64)
+
+## Running
+
+```bash
+# Standard 8xH100 run
+torchrun --nproc_per_node=8 train_gpt.py
+
+# Ablation: baseline without Verifily
+VERIFILY_ENABLED=0 torchrun --nproc_per_node=8 train_gpt.py
+
+# Ablation: Verifily without salience
+VERIFILY_SALIENCE=0 torchrun --nproc_per_node=8 train_gpt.py
+
+# Ablation: Verifily without eval mixer
+VERIFILY_MIXER=0 torchrun --nproc_per_node=8 train_gpt.py
+```

records/track_10min_16mb/2026-04-05_Verifily_DataQuality_ThreeTier_DCLS/requirements.txt

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+# FlashAttention 3 must be installed separately; see README.md
+sentencepiece
+zstandard

records/track_10min_16mb/2026-04-05_Verifily_DataQuality_ThreeTier_DCLS/submission.json

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+{
+  "author": "verifily",
+  "github_id": "verifily",
+  "name": "Verifily Data Quality: Three-Tier Token Weighting + DCLS Salience + Complementary Eval",
+  "blurb": "Layered on top of SOTA XSA+GPTQ+BigramHash baseline. Three data-quality components validated locally (M4 Max, -0.63% BPB vs baseline): (1) GPU-resident bigram statistics for complementary training — tokens predictable by bigrams get 0.10 weight (soft nudge), freeing neural capacity for harder tokens. (2) Three-tier token classification: Predictable (0.10w, top ~5%), Frontier (1.0w), Noise (0.70w) based on bigram probability + document quality signals (vocabulary richness, repetition). Data-driven thresholds (~p95 bigram, median quality). (3) DCLS-inspired salience tracker: surprise-driven batch loss multiplier [0.85, 1.15]. (4) Quality-conditioned eval-time bigram mixer: alpha conditioned on document quality.",
+  "date": "2026-04-05",
+  "track": "10min_16mb",
+  "val_loss": null,
+  "val_bpb": null,
+  "seeds": [42, 314, 999],
+  "hardware": "8xH100 80GB SXM",
+  "technique_summary": "Verifily three-tier token weighting + DCLS salience + quality-conditioned bigram mixer, on SOTA XSA+GPTQ+BigramHash base"
+}