Record: Per-Order Adaptive Alpha + N-gram Backoff (val_bpb=0.2995, 3-seed)

records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/README.md

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+## EMA-GPU + Multi-Order N-gram Backoff + Pre-Enrichment + XSA
+
+**val_bpb: 0.2995** (3-seed mean, std 0.0016) | 14.94 MB | 8xH100 SXM, 600s
+
+---
+
+### 3-Seed Results
+
+| Seed | Steps | Sliding BPB | val_bpb | Artifact |
+|---|---|---|---|---|
+| 1337 | 9,268 | 1.1478 | 0.3001 | 14,942,971 |
+| 42 | 9,318 | 1.1468 | 0.2977 | 14,922,769 |
+| 3011 | 9,322 | 1.1463 | 0.3008 | 14,939,305 |
+| **Mean** | — | **1.1470** | **0.2995** | — |
+| **Std** | — | — | **0.0016** | — |
+
+---
+
+### Architecture
+
+10L/512d U-Net, 25.25M params. GQA 8H/4KV, MLP 3x (1536 hidden), tied embeddings, logit softcap=30.0.
+
+- **GELU Pre-Enrichment** (512→768→512): Wider nonlinear transformation before transformer blocks. Embedding → BigramHash add → SmearGate → Linear(512→768) → GELU → Linear(768→512) → RMS Norm → blocks.
+- **XSA** (last 4 layers): Exclusive Self Attention removes self-value bias via orthogonal projection (arXiv:2603.09078, GQA-aware implementation from PR #265 @unnir). Zero parameters.
+- **SmearGate**: Per-dim gate blending each token with previous token's embedding. F.pad for efficiency.
+- **BigramHash** (2048×128): Hash-table embedding for token bigrams, projected to model dim.
+- **U-Net skip connections**: Encoder-decoder with learnable skip weights.
+
+Training: Muon+AdamW, WD=0.04, matrix_lr=0.025, scalar_lr=0.025, warmdown=3500 iters, batch=524K tokens, seq=2048. EMA decay=0.997. Int6 QAT + lzma (preset=6).
+
+---
+
+### EMA on GPU (37% faster training) — novel contribution
+
+EMA state kept on GPU during training instead of synchronous GPU→CPU copy every step. Only moved to CPU at the end for serialization. To my knowledge, this optimization is not used in other submissions.
+
+Step time: **64.4ms** (vs 101ms before). Enables **9,312 steps** in 600s vs ~5,900 before — 57% more gradient updates from the same training time.
+
+---
+
+### Multi-Order N-gram Backoff (score-first, backward-looking)
+
+Multi-order n-gram backoff with entropy-adaptive alpha during sliding window eval. Concept credited to @deanbrr (PR #659), developed by PR #706 (@newjordan) and PR #727 (@Asukabot0).
+
+**Protocol:**
+- Multi-order backoff: orders 7→6→5→4→3→2, first hit with count≥2 wins
+- Entropy-adaptive alpha: `alpha = 0.05 + 0.55 * sigmoid(2 * (H - 4.0))`
+- High model entropy → trust n-gram more; low entropy → trust model
+- Cache built from already-scored tokens only (backward-looking)
+- Score-first: cache updated AFTER segment scoring
+- Dual-array hash scheme: separate context count and pair count arrays per order (4M buckets each)
+- Per-GPU independent cache, no cross-GPU sync
+- Hash tables precomputed for all orders in single pass
+- Integrated into sliding window eval (single pass)
+
+**Compliance:**
+- Score-first, backward-looking: n-gram counts built from previously scored tokens only
+- No oracle selection: alpha depends solely on model's own entropy, never on ground-truth
+- No cross-GPU sync: each GPU maintains its own independent cache
+
+**Improvement:** 1.1478 → 0.3001 = **-0.848 BPB**
+
+#### Pre-Enrichment Confidence Modulation
+
+Uses the pre-enrichment layer's transformation magnitude as a confidence signal. High delta = model uncertain about this context = trust n-gram more. Low delta = model confident = trust model more. Modulates entropy-adaptive alpha by `(0.5 + 1.0 * pe_conf)`.
+
+---
+
+### Toggleable Features (default OFF, not used in this submission)
+
+- `VALUE_RESIDUAL=1` — Layer-0 V mixed into all subsequent layers via learned sigmoid gates
+- `GATED_ATTN=1` — Per-head sigmoid gates on attention output
+
+---
+
+### Reproduce
+
+```bash
+python3 data/cached_challenge_fineweb.py --variant sp1024 --train-shards 80
+torchrun --standalone --nproc_per_node=8 train_gpt.py
+```
+
+All defaults baked in. No env vars needed. 8xH100 SXM, 600s training + ~182s eval.
+
+---
+
+### Credits
+- Muon optimizer — modded-nanogpt baseline (kellerjordan)
+- SmearGate + BigramHash — PR #65 (@aquariouseworkman)
+- XSA — arXiv:2603.09078; GQA-aware PR #265 (@unnir)
+- EMA + GPTQ-lite + warmdown tuning — PR #414 (@signalrush)
+- N-gram eval cache — concept PR #659 (@deanbrr); fixed 5-gram PR #706 (@newjordan); multi-order entropy-adaptive PR #727 (@Asukabot0)
+- Shared GPU n-gram cache — PR #796 (@Robby955); chunk-synchronized PR #800 (@newjordan); PR #809 (@AayushBaniya2006)
+- Per-order adaptive alpha — PR #798 (@travispchen); Cubric scaling PR #800 (@newjordan)
+- Overtone init — modded-nanogpt baseline
+- GELU Pre-Enrichment — original to this submission
+- EMA on GPU — original to this submission
+- Pre-Enrichment Confidence Modulation — original to this submission
+
+### Included Files
+
+- `train_gpt.py` — standalone training script with all modifications
+- `train.log` — full 8xH100 training + eval log (seed 1337)
+- `submission.json` — leaderboard metadata
+- `README.md` — this file

records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/submission.json

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+{
+  "author": "Idanr",
+  "github_id": "idan3011",
+  "name": "Two-Phase Shared N-gram Cache + EMA-GPU + Pre-Enrichment + XSA",
+  "blurb": "Two-phase eval: distributed GPU forward + global sequential n-gram cache (orders 2-11). Per-order adaptive alpha + PE confidence modulation. EMA on GPU (64.7ms/step). 10L 512d. 3-seed mean 0.2995 (std 0.0016).",
+  "date": "2026-03-26T07:45:00Z",
+  "val_loss": 1.93793804,
+  "val_bpb": 0.29953465,
+  "val_bpb_seeds": [0.30008709, 0.29765751, 0.30079936],
+  "val_bpb_std": 0.0016,
+  "pre_quant_val_loss": 1.9663,
+  "pre_quant_val_bpb": 1.1646,
+  "step_stop": 9268,
+  "wallclock_seconds": 600.031,
+  "eval_time_seconds": 204.138,
+  "bytes_total": 14942971,
+  "bytes_model_int6_lzma": 14878748,
+  "bytes_code": 66581
+}

records/track_10min_16mb/2026-03-20_PreEnrich_EncoderRecurrence/train.log

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+W0326 02:39:19.172000 34413 torch/distributed/run.py:803]
+W0326 02:39:19.172000 34413 torch/distributed/run.py:803] *****************************************
+W0326 02:39:19.172000 34413 torch/distributed/run.py:803] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed.
+W0326 02:39:19.172000 34413 torch/distributed/run.py:803] *****************************************
+logs/0d771539-26db-4427-b5a8-0a4c24bd56ad.txt
+val_bpb:enabled tokenizer_kind=sentencepiece tokenizer_path=./data/tokenizers/fineweb_1024_bpe.model
+train_loader:dataset:fineweb10B_sp1024 train_shards:80
+val_loader:shards pattern=./data/datasets/fineweb10B_sp1024/fineweb_val_*.bin tokens:62021632
+model_params:25254992
+world_size:8 grad_accum_steps:1
+sdp_backends:cudnn=True flash=True mem_efficient=False math=False
+attention_mode:gqa num_heads:8 num_kv_heads:4
+tie_embeddings:True embed_lr:0.035 head_lr:0.0 matrix_lr:0.025 scalar_lr:0.025
+train_batch_tokens:524288 train_seq_len:2048 iterations:20000 warmup_steps:20 max_wallclock_seconds:600.000
+seed:1337
+warmup_step:1/20
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+stopping_early: wallclock_cap train_time:600031ms step:9268/20000
+peak memory allocated: 13058 MiB reserved: 13280 MiB
+swa: averaging 14 checkpoints on top of EMA
+ema: loading weights
+Serialized model: 99486509 bytes
+Code size: 64223 bytes
+Total submission size: 99550732 bytes
+Serialized model int6+lzma: 14878748 bytes (payload:25993024 raw_torch:26045291 payload_ratio:3.83x)
+Total submission size int6+lzma: 14942971 bytes
+final_int8_zlib_roundtrip val_loss:1.9738 val_bpb:1.1690 eval_time:2054ms
+final_int8_zlib_roundtrip_exact val_loss:1.97382834 val_bpb:1.16901232
+final_sliding_window sliding_bpb:1.1478 val_bpb:0.3001 eval_time:204138ms
+final_sliding_window_exact sliding_bpb:1.14775606 val_bpb:0.30008709