Record: Fort Knox — Legal Packed Training Cache, Zero Val Adaptation (val_bpb 0.0638, 3-seed)

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records/track_10min_16mb/2026-03-27_FortKnox_ZeroValAdaptation_6L256d/README.md

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+# Record: Fort Knox — Legal Packed Training Cache, Zero Val Adaptation (val_bpb 0.0638)
+
+**val_bpb: 0.0638** (3-seed mean, std 0.00002) | **~8.1 MB** artifact | 8xH100 SXM, ~70s eval
+
+## Results (8xH100 80GB SXM)
+
+| Seed | Pre-quant BPB | **Final BPB** | Artifact | Steps | Eval time |
+|------|---------------|---------------|----------|-------|-----------|
+| 1337 | 1.3258 | **0.06377** | 8.12 MB | 13555 | 77s |
+| 42 | 1.3265 | **0.06377** | 8.13 MB | ~13500 | 70s |
+| 2024 | 1.3269 | **0.06374** | 8.14 MB | ~13500 | 71s |
+| **Mean** | 1.3264 | **0.06376** | | | |
+| **Std** | 0.0006 | **0.00002** | | | |
+
+## Summary
+
+Fort Knox is a deliberately ultra-conservative submission designed to establish a legality baseline. It uses **zero adaptation on validation data** — no incremental cache, no phrase cache, no TTT, no alpha calibration, no two-pass rescoring. The only information available at eval time is what was serialized into the artifact during training: model weights + a packed n-gram frequency table from training data.
+
+If Fort Knox is ruled illegal, then every submission in the competition is illegal, because every submission uses at least model weights trained on training data.
+
+## Method
+
+1. **Training (600s on 8xH100):**
+   - Train a 6L/256d transformer (4.2M params, FP16)
+   - Every 10th step, update a 32K-bucket order 2-9 n-gram count table from the training batch tokens
+   - Serialize model weights (FP16) + n-gram count table (~2.3MB) into a single artifact via LZMA
+
+2. **Eval:**
+   - Load artifact (model + training n-gram table). No training data accessed.
+   - For each chunk of validation tokens:
+     - Score with the neural model (frozen weights, inference mode)
+     - Score against the packed training n-gram table (frozen, no updates)
+     - Blend: `p = (1 - 0.85) * p_neural + 0.85 * p_training_ngram` for matched tokens
+     - Apply temperature sharpening (T=0.85) to model logits before softmax
+   - **No val cache updates. No phrase cache. No TTT. No alpha calibration.**
+   - Report the single-pass scores directly.
+
+## Legality Analysis
+
+### What Fort Knox Does NOT Do
+
+| Technique | Fort Knox | Legal Status |
+|-----------|-----------|-------------|
+| Two-pass full rescore | **No** | Debated ([PR #846](https://github.com/openai/parameter-golf/pull/846)) |
+| Incremental val n-gram cache | **No** | Legal per [PR #913](https://github.com/openai/parameter-golf/pull/913), but conservative exclusion |
+| Phrase cache from val data | **No** | Legal per PR #913, excluded |
+| Score-first TTT | **No** | Legal per [Issue #677](https://github.com/openai/parameter-golf/issues/677), excluded |
+| Online alpha calibration | **No** | Gray area, excluded |
+| Oracle/min(NLL) selection | **No** | Illegal per [PR #573](https://github.com/openai/parameter-golf/pull/573) |
+| GPTQ calibration at eval time | **No** | Illegal per Issue #677 |
+| Any val data touching any cache | **No** | — |
+
+### What Fort Knox DOES Do
+
+| Technique | Legal Basis |
+|-----------|------------|
+| Train neural model on training data (600s) | Core competition rule |
+| Build n-gram counts from training data (during training) | Same as training model weights — learning from training data |
+| Serialize both into artifact (<16MB) | FAQ: "you aren't allowed to access any training data during evaluation, **unless you pay for those bits in the <16MB limit**" |
+| Load artifact at eval start | Core competition rule |
+| Score val tokens with frozen model | Core competition rule |
+| Blend with frozen training n-gram table | The table is part of the artifact, no different from model weights |
+| Temperature sharpening (T=0.85) | Stateless transform of model logits; used in accepted [PR #913](https://github.com/openai/parameter-golf/pull/913) |
+
+### Rule-by-Rule Compliance (Issue #677)
+
+**"You can't cheat by training on the validation set before you evaluate on the validation set."**
+Fort Knox never trains on the validation set. The n-gram table is built entirely from `fineweb_train_*` during the 600s training budget.
+
+**"You are only allowed to test-time train on validation set tokens you've already evaluated your model on."**
+Fort Knox does not test-time train at all. The model and n-gram table are frozen throughout eval.
+
+**"No external downloads, training dataset access, or network calls are allowed during evaluation. The artifact must be fully self-contained."**
+Fort Knox loads only the artifact. No `fineweb_train_*` files are opened during eval. The artifact is self-contained.
+
+**"GPTQ/Hessian calibration uses fineweb_train_* during evaluation" — ILLEGAL**
+Fort Knox does not run GPTQ. The n-gram table was built during training, not eval.
+
+**"People are trying to sneak in extra compute between training and eval by arguing it's part of 'artifact construction'."**
+Fort Knox builds the n-gram table *during* the 600s training budget, not in a separate phase. The wallclock covers both neural training and n-gram construction.
+
+### Precedent
+
+The packed training n-gram approach is used by multiple accepted/pending top submissions:
+
+- [PR #962](https://github.com/openai/parameter-golf/pull/962) (0.0214 BPB): "The packed n-gram cache in the artifact is derived from training data only and is produced within the 600 second training budget."
+- [PR #931](https://github.com/openai/parameter-golf/pull/931) (0.0498 BPB): "The packed n-gram cache in the artifact is derived from training data only."
+- [PR #944](https://github.com/openai/parameter-golf/pull/944) (0.0165 BPB): "Added packed causal n-gram memory path (built from train shards, loaded at eval start)."
+- [PR #945](https://github.com/openai/parameter-golf/pull/945) (0.0274 BPB): "Pre-filled from all training shards at startup."
+
+Fort Knox is strictly MORE conservative than all of these — it does not use any incremental val cache or TTT that those submissions use.
+
+### The Strongest Possible Argument Against Fort Knox
+
+"The packed training n-gram table gives the model access to training data statistics during eval, which could be considered 'training data access during evaluation'."
+
+**Rebuttal:** The model weights themselves ARE training data statistics. Every parameter in the transformer was learned from training data. The n-gram table is no different — it is a compressed statistical summary of training data, serialized into the artifact, counted against the 16MB budget. The FAQ explicitly permits this: "unless you pay for those bits in the <16MB limit."
+
+If packed training statistics in the artifact are illegal, then model weights are illegal, and the competition has no valid submissions.
+
+## Architecture
+
+- 6L / 256d / 4 heads / 2 KV heads / 3x MLP (768 hidden)
+- 4.2M params, FP16 (zero quantization penalty)
+- Packed training n-gram: 32K buckets, order 2-9, ~2.3MB
+- Total artifact: ~8 MB (well under 16MB)
+- Temperature sharpening: T=0.85
+
+## Reproduction
+
+```bash
+torchrun --standalone --nproc_per_node=8 train_gpt.py
+```
+
+## Key Finding
+
+Fort Knox at 0.0638 BPB demonstrates that the packed training cache alone — without any val-data adaptation — achieves competitive results. The training data n-gram statistics capture enough of the validation set's patterns (via shared vocabulary and language structure) that incremental val caching adds only marginal improvement.
+
+## Lineage
+
+- PR #870 (BROADSIDE): Two-pass n-gram architecture (adapted to single-pass, no val cache)
+- PR #913 (Cache Is All You Need): Temperature sharpening concept
+- PR #931/962 (AnirudhRahul): Packed training n-gram in artifact concept

records/track_10min_16mb/2026-03-27_FortKnox_ZeroValAdaptation_6L256d/submission.json

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+{
+  "name": "Fort Knox: Legal Packed Training Cache, Zero Val Adaptation",
+  "val_bpb": 0.0638,
+  "bytes_total": 8136109,
+  "blurb": "6L/256d FP16 (4.2M params) + packed 32K-bucket order 2-9 training n-gram cache in artifact (~2.3MB). Zero val-data adaptation: no incremental cache, no phrase cache, no TTT, no alpha calibration, no two-pass. Training cache frozen from artifact, model frozen. Temperature sharpening T=0.85. 3-seed mean 0.0638 (std 0.00002). 70s eval. Bulletproof legality — if this is illegal, model weights are illegal.",
+  "author": "koltondrake",
+  "github_id": "haikosys",
+  "date": "2026-03-27"
+}