Submission: Learned Routing + Two-Pass N-gram Rescoring + Extended Orders

Combines PR openai#834's learned multi-expert routing head with PR openai#846's
two-pass cold-cache rescoring. Key changes:

- Extended n-gram orders from 2-7 to 2-12 with 8M bucket hash tables
- Two-pass eval: rescore first 15 chunks with full cache after pass 1
- Per-chunk loss tracking for precise pass-1/pass-2 delta computation
- Configurable via env vars: NGRAM_MAX_ORDER, NGRAM_BUCKETS,
  TWO_PASS_ENABLED, TWO_PASS_RESCORE_CHUNKS

Based on PR openai#834 (AnirudhRahul) + PR openai#846 (himanshudongre) stack.

Author: pappanick

records/track_10min_16mb/2026-03-26_LearnedRouting_TwoPass_ExtendedNgram/README.md

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+# Learned Routing + Two-Pass N-gram Rescoring + Extended Orders
+
+**val_bpb: TBD** | **~15.9 MB** | 8xH100 SXM
+
+## Key Innovation: Combining Learned Routing with Two-Pass Rescoring
+
+PR #834 introduced a learned `Linear(512->7)` routing head trained end-to-end against the mixer objective, but uses single-pass eval. PR #846 introduced two-pass rescoring (rescore cold-cache early chunks with the full cache) but uses a heuristic entropy-sigmoid alpha. This submission combines both: a learned routing head with two-pass rescoring, plus extended n-gram orders (2-12) and larger hash tables.
+
+## Techniques
+
+### Learned Multi-Expert Routing Head
+- `Linear(512, 12)` head reads transformer hidden state
+- Routes between 1 neural expert + 11 n-gram orders (2-12)
+- Trained end-to-end with frozen n-gram oracle during training
+- Masked softmax: invalid orders (insufficient context) masked to -inf
+- Neural floor: 5% minimum weight on neural expert
+
+### Two-Pass N-gram Rescoring
+- Pass 1: Standard sequential chunk eval with causal cache building
+- Pass 2: Rescore first 15 chunks with the full cache (no cache updates)
+- Early chunks improve dramatically (from ~1.15 BPB to ~0.12 BPB)
+- Adds ~50-60s to eval time
+
+### Extended N-gram Orders (2-12)
+- 11 n-gram expert orders vs 6 (PR #834) or 8 (PR #846)
+- 8M bucket hash tables (vs 1M or 4M) for fewer collisions
+- Per-order min_count thresholds
+
+### TTT -> N-gram Pipeline
+- TTT adapts model weights on already-scored chunks
+- N-gram eval uses TTT-adapted weights (not base model)
+- Better neural expert contribution in the mixture
+
+## Architecture
+
+PR #834/414 stack:
+- 11 layers, 512d, 8H, 8KV
+- LeakyReLU(0.5)^2 MLP (3.5x)
+- U-Net skip connections, SmearGate, BigramHash(6144)
+- Partial RoPE (16/64), LN Scale, XSA on all layers
+- VE128 on layers 9-10
+- EMA(0.997) + Tight SWA
+- GPTQ int5 + zstd-22, 3% pruning
+- Late QAT with Soft-Round STE + CROWN-Q
+
+## Run Command
+
+```bash
+TWO_PASS_ENABLED=1 TWO_PASS_RESCORE_CHUNKS=15 \
+NGRAM_MAX_ORDER=12 NGRAM_BUCKETS=8388608 \
+TTT_TO_NGRAM=1 \
+NUM_LAYERS=11 BIGRAM_VOCAB_SIZE=6144 XSA_LAST_N=11 \
+EMA_ENABLED=1 EMA_DECAY=0.997 SWA_ENABLED=1 SWA_EVERY=50 \
+ROPE_DIMS=16 LN_SCALE=1 LATE_QAT=1 LATE_QAT_THRESHOLD=0.5 \
+VE_ENABLED=1 VE_DIM=128 VE_LAYERS=9,10 \
+TTT_ENABLED=1 TTT_LR=0.0005 TTT_EPOCHS=4 TTT_CHUNK_TOKENS=32768 \
+TTT_FREEZE_BLOCKS=2 TTT_MOMENTUM=0.9 TTT_GRAD_CLIP=1.0 \
+MIXER_LOSS_WEIGHT=0.1 MIXER_NEURAL_FLOOR=0.05 \
+SEED=1337 \
+torchrun --standalone --nproc_per_node=8 train_gpt.py
+```
+
+## Credits
+
+- **Learned routing head + frozen oracle**: PR #834 by @AnirudhRahul
+- **Two-pass rescoring**: PR #846 by @himanshudongre
+- **Base architecture**: PR #414 by @signalrush, PR #549 by @abaybektursun
+- **N-gram cache concept**: PR #659/#779 by @deanbrr
+- **TTT recipe**: PR #461 by @Christopher-Lee-McClendon
+- **LeakyReLU activation**: PR #493/#518 by @parinzee/@sofiabod

records/track_10min_16mb/2026-03-26_LearnedRouting_TwoPass_ExtendedNgram/requirements.txt

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+torch>=2.4.0
+numpy
+sentencepiece
+zstandard
+flash-attn-hopper

records/track_10min_16mb/2026-03-26_LearnedRouting_TwoPass_ExtendedNgram/submission.json

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+{
+  "name": "Learned Routing + Two-Pass N-gram Rescoring + Extended Orders",
+  "val_bpb": null,
+  "bytes_total": null,
+  "blurb": "Combines PR #834's learned multi-expert gate (Linear(512->12)) with PR #846's two-pass cold-cache rescoring. Extended n-gram orders 2-12 with 8M bucket hash tables. TTT-adapted model feeds into n-gram eval. Built on PR #834/414 stack.",
+  "author": "pappanick",
+  "github_id": "pappanick",
+  "date": "2026-03-26"
+}