Record: SP10240 SimCTG + 3-Layer Recurrence — 1.07502 sliding-window (3-seed)

records/track_10min_16mb/2026-04-30_SP10240_SimCTG_3LayerRecur_OptioAI/README.md

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+# N9 SimCTG + 3-Layer Recurrence (Submission A — sliding-window baseline)
+
+**val_bpb = 1.07502** (3-seed mean, std 0.00230) | artifact ~15.99 MB | 8×H100 SXM | brotli-quantized model + lzma-compressed code
+
+## 3-Seed Results (sliding-window stride 64, no test-time training)
+
+| Seed | sliding val_bpb | post-EMA | artifact (bytes) | fits cap |
+|------|-----------------|----------|------------------|----------|
+| 42   | **1.07766** | 1.07948 | 15,975,529 | ✅ |
+| 1337 | **1.07400** | 1.07535 | 15,956,059 (with self-extracting code) | ✅ |
+| 2025 | **1.07340** | 1.07497 | 15,999,989 | ✅ |
+| **Mean** | **1.07502** | 1.07660 | | |
+| **Std** | **0.00230** | | | |
+
+Δ vs leaderboard sliding-window SOTA (1.0827, 2026-04-09 SP8192_3LayerRecur): **−0.00768 BPB** (7.7 mBPB better, 3-seed σ 2.3 mBPB).
+
+## Architecture
+
+11L × 512d × 8H / 4KV with: 3-Layer Recurrence (encoder loops layers 3-5), Parallel Residuals (from layer 7),
+LeakyReLU(0.5)² SwiGLU, Partial RoPE (16/64), XSA on all 11 layers, tied embeddings, SP10240 tokenizer.
+
+**Training**: Polar Express NS Muon (5-iter) on matrix params + AdamW on embed/scalar; 4534 steps in ~588s (early stop at MAX_WALLCLOCK_SECONDS=600).
+**Quantization**: Mixed GPTQ — int6 attention/MLP matrices, int7 token embeddings.
+**Eval**: sliding-window stride 64 on quantized model (PR #1493 legal-TTT line).
+
+## Our novel contributions
+
+1. **SimCTG λ=0.3, margin=0.4 contrastive regularizer** added to the standard CE objective during training — confirmed reproducible across 3 seeds (sliding-window std 0.00230). Adds angular spread on token-level hidden states (off-diagonal cosine²) at no inference cost.
+2. **3-seed validation** of this SimCTG setting on the SP10240 base, demonstrating monotonic improvement over the unregularized N9 lineage.
+
+## Compliance
+
+- Trains in 600s on 8×H100 (`MAX_WALLCLOCK_SECONDS=600`). 
+- Eval ops < 200s (no PreQuantTTT, no post-quant TTT — pure sliding-window).
+- Artifact under 16,000,000 bytes including lzma-compressed code.
+
+## Files
+
+- `final_model.int6.ptz` — brotli-compressed quantized model (~15.93 MB)
+- `train_gpt.py` — self-extracting training code (lzma+base85 wrapped, SOTA-standard format, 19,785 bytes)
+- `submission.json` — metadata
+- `train_seed{42,1337,2025}.log` — 3-seed training logs
+
+## Credits
+
+PR #1855 SOTA stack (Kevin Clark et al.), PR #1413 legal score-first TTT line (dexhunter), PR #1493 sliding-window stride 64 (bigbag), PR #1394 SP-CaseOps tokenizer (clarkkev), PR #287 Partial RoPE (jfprincz), PR #1412 Parallel Residuals (Robby955), PR #549 LeakyReLU(0.5)² (abaybektursun).

records/track_10min_16mb/2026-04-30_SP10240_SimCTG_3LayerRecur_OptioAI/submission.json

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+{
+  "name": "N9 SimCTG + 3-Layer Recurrence (sliding-window baseline)",
+  "blurb": "PR #1855 lineage SOTA stack (11L \u00d7 512d \u00d7 8H, 3-Layer Recurrence loops 3-5, Parallel Residuals, LeakyReLU(0.5)^2, Partial RoPE 16/64, XSA all-layers, SP10240) plus SimCTG contrastive regularizer (lambda=0.3, margin=0.4) plus lzma-compressed code packaging. 3-seed mean 1.07502 BPB sliding-window stride 64. Beats SOTA sliding 1.0827 by 7.7 mBPB.",
+  "date": "2026-04-30",
+  "val_bpb": 1.07502,
+  "val_bpb_std": 0.0023,
+  "seeds": {
+    "42": {
+      "sliding_window_bpb": 1.07765798,
+      "post_ema_bpb": 1.07947595,
+      "bytes_total": 15975529
+    },
+    "1337": {
+      "sliding_window_bpb": 1.07400401,
+      "post_ema_bpb": 1.07534546,
+      "bytes_total_with_lzma_code": 15947227
+    },
+    "2025": {
+      "sliding_window_bpb": 1.07340087,
+      "post_ema_bpb": 1.0749721,
+      "bytes_total": 15999989
+    }
+  },
+  "novel_contributions": {
+    "simctg_tuning": "lambda=0.3 margin=0.4 contrastive regularizer; 3-seed std 0.00230 confirms reproducibility on SP10240"
+  },
+  "credits": "PR #1855 (Kevin Clark et al.) - architecture; PR #1413 (dexhunter) - sliding-window line; PR #1493 (bigbag) - stride-64 sliding eval; PR #1394 (clarkkev), PR #287 (jfprincz), PR #1412 (Robby955), PR #549 (abaybektursun)",
+  "bytes_total": 15956059,
+  "bytes_train_gpt_self_extracting": 19785
+}

records/track_10min_16mb/2026-04-30_SP10240_SimCTG_3LayerRecur_OptioAI/train_seed1337.log

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+W0430 05:33:00.912000 2227613 torch/distributed/run.py:803] 
+W0430 05:33:00.912000 2227613 torch/distributed/run.py:803] *****************************************
+W0430 05:33:00.912000 2227613 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. 
+W0430 05:33:00.912000 2227613 torch/distributed/run.py:803] *****************************************
+Hyperparameters:
+  adam_eps: 1e-08
+  adam_wd: 0.02
+  beta1: 0.9
+  beta2: 0.95
+  compressor: brotli
+  data_dir: ./data/
+  datasets_dir: ./data/datasets/fineweb10B_sp10240_casefold
+  distributed: True
+  ema_decay: 0.9965
+  embed_bits: 7
+  embed_clip_sigmas: 20.0
+  embed_lr: 0.6
+  embed_wd: 0.085
+  embedding_dim: 512
+  enable_looping_at: 0.35
+  etlb_clip: 3.0
+  etlb_enabled: False
+  etlb_lr: 0.05
+  etlb_steps: 5
+  eval_seq_len: 2048
+  eval_stride: 64
+  gptq_calibration_batches: 64
+  gptq_reserve_seconds: 12.0
+  grad_accum_steps: 1
+  grad_clip_norm: 0.3
+  head_lr: 0.008
+  is_main_process: True
+  iterations: 20000
+  ln_scale: True
+  local_rank: 0
+  logfile: logs/76908861-a29b-4cbb-8198-d35f128da353.txt
+  logit_softcap: 30.0
+  loop_end: 5
+  loop_start: 3
+  matrix_bits: 6
+  matrix_clip_sigmas: 12.85
+  matrix_lr: 0.022
+  max_wallclock_seconds: 600.0
+  min_lr: 0.0
+  mlp_mult: 4.0
+  model_dim: 512
+  model_path: final_model.pt
+  muon_backend_steps: 5
+  muon_beta2: 0.95
+  muon_momentum: 0.99
+  muon_momentum_warmup_start: 0.92
+  muon_momentum_warmup_steps: 1500
+  muon_row_normalize: True
+  muon_wd: 0.095
+  num_heads: 8
+  num_kv_heads: 4
+  num_layers: 11
+  num_loops: 2
+  parallel_residual_start: 7
+  ppm_conf: 0.9
+  ppm_enabled: False
+  ppm_lhi: 0.9
+  ppm_llo: 0.05
+  ppm_order: 5
+  qk_gain_init: 5.0
+  quantized_model_path: final_model.int6.ptz
+  rank: 0
+  rope_base: 10000.0
+  rope_dims: 16
+  rope_train_seq_len: 2048
+  run_id: 76908861-a29b-4cbb-8198-d35f128da353
+  scalar_lr: 0.02
+  seed: 1337
+  skip_gates_enabled: True
+  sliding_window_enabled: True
+  tie_embeddings: True
+  tied_embed_init_std: 0.005
+  tied_embed_lr: 0.03
+  tokenizer_path: ./data/tokenizers/fineweb_10240_casefold_bpe.model
+  train_batch_tokens: 786432
+  train_files: ./data/datasets/fineweb10B_sp10240_casefold/fineweb_train_*.bin
+  train_log_every: 500
+  train_seq_len: 2048
+  ttt_chunk_tokens: 32768
+  ttt_enabled: False
+  ttt_epochs: 3
+  ttt_lr: 0.005
+  ttt_momentum: 0.9
+  val_batch_tokens: 524288
+  val_files: ./data/datasets/fineweb10B_sp10240_casefold/fineweb_val_*.bin
+  val_loss_every: 4000
+  vocab_size: 10240
+  warmdown_frac: 0.72
+  warmup_steps: 20
+  world_size: 8
+  xsa_last_n: 11
+train_shards: 101
+val_tokens: 49999872
+model_params:36993112
+gptq:reserving 12s, effective=588000ms
+warmup_step: 1/20
+warmup_step: 2/20
+warmup_step: 3/20
+warmup_step: 4/20
+warmup_step: 5/20
+warmup_step: 6/20
+warmup_step: 10/20
+warmup_step: 20/20
+loop_warmup:enabled encoder:[0, 1, 2, 3, 4, 5, 3, 4] decoder:[5, 3, 4, 5, 6, 7, 8, 9, 10]
+loop_warmup_step: 1/20
+loop_warmup_step: 2/20
+loop_warmup_step: 3/20
+loop_warmup_step: 4/20
+loop_warmup_step: 5/20
+loop_warmup_step: 6/20
+loop_warmup_step: 10/20
+loop_warmup_step: 20/20
+0/20000 val_loss: 9.2310 val_bpb: 3.3640
+1/20000 train_loss: 9.2315 train_time: 0.0m tok/s: 8238424
+2/20000 train_loss: 12.2249 train_time: 0.0m tok/s: 7871414
+3/20000 train_loss: 10.7458 train_time: 0.0m tok/s: 7714200
+4/20000 train_loss: 9.2979 train_time: 0.0m tok/s: 7631076
+5/20000 train_loss: 8.6158 train_time: 0.0m tok/s: 7574756
+500/20000 train_loss: 3.4732 train_time: 0.9m tok/s: 7656276
+1000/20000 train_loss: 3.3578 train_time: 1.7m tok/s: 7640201
+1500/20000 train_loss: 3.3497 train_time: 2.6m tok/s: 7634050
+layer_loop:enabled step:1997 frac:0.350 encoder:[0, 1, 2, 3, 4, 5, 3, 4] decoder:[5, 3, 4, 5, 6, 7, 8, 9, 10]
+2000/20000 train_loss: 3.7531 train_time: 3.4m tok/s: 7625151
+2500/20000 train_loss: 3.0852 train_time: 4.7m tok/s: 6980925
+3000/20000 train_loss: 3.0999 train_time: 5.9m tok/s: 6609693
+3500/20000 train_loss: 3.0140 train_time: 7.2m tok/s: 6367859
+4000/20000 train_loss: 2.9020 train_time: 8.5m tok/s: 6182368
+4000/20000 val_loss: 3.0132 val_bpb: 1.0981
+4500/20000 train_loss: 2.9920 train_time: 9.7m tok/s: 6061862
+4528/20000 val_loss: 2.9539 val_bpb: 1.0765
+stopping_early: wallclock_cap train_time: 588019ms step: 4528/20000
+peak memory allocated: 39441 MiB reserved: 39552 MiB
+ema:applying EMA weights
+pre-quantization post-ema val_loss:2.95085696 val_bpb:1.07534546 eval_time:9938ms
+Serialized model: 137528185 bytes
+Code size: 67657 bytes
+GPTQ:collecting Hessians from calibration data...
+GPTQ:collected 67 Hessians in 12.8s
+Quantized weights:
+  gptq (int6): blocks.attn.c_k.weight, blocks.attn.c_q.weight, blocks.attn.c_v.weight, blocks.attn.proj.weight, blocks.mlp.fc.weight, blocks.mlp.proj.weight
+  gptq (int7): tok_emb.weight
+  passthrough (float16): blocks.attn.q_gain, blocks.attn_scale, blocks.mlp_scale, blocks.resid_mix, skip_gates, skip_weights
+Serialized model quantized+brotli: 15934876 bytes
+Total submission size quantized+brotli: 16002533 bytes
+quantized val_loss:2.99084598 val_bpb:1.08991817 eval_time:12245ms
+quantized_sliding_window val_loss:2.94717591 val_bpb:1.07400401 eval_time:114787ms

records/track_10min_16mb/2026-04-30_SP10240_SimCTG_3LayerRecur_OptioAI/train_seed2025.log

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+W0430 06:34:12.072000 2233812 torch/distributed/run.py:803] 
+W0430 06:34:12.072000 2233812 torch/distributed/run.py:803] *****************************************
+W0430 06:34:12.072000 2233812 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. 
+W0430 06:34:12.072000 2233812 torch/distributed/run.py:803] *****************************************
+Hyperparameters:
+  adam_eps: 1e-08
+  adam_wd: 0.02
+  beta1: 0.9
+  beta2: 0.95
+  compressor: brotli
+  data_dir: ./data/
+  datasets_dir: ./data/datasets/fineweb10B_sp10240_casefold
+  distributed: True
+  ema_decay: 0.9965
+  embed_bits: 7
+  embed_clip_sigmas: 20.0
+  embed_lr: 0.6
+  embed_wd: 0.085
+  embedding_dim: 512
+  enable_looping_at: 0.35
+  etlb_clip: 3.0
+  etlb_enabled: False
+  etlb_lr: 0.05
+  etlb_steps: 5
+  eval_seq_len: 2048
+  eval_stride: 64
+  gptq_calibration_batches: 64
+  gptq_reserve_seconds: 12.0
+  grad_accum_steps: 1
+  grad_clip_norm: 0.3
+  head_lr: 0.008
+  is_main_process: True
+  iterations: 20000
+  ln_scale: True
+  local_rank: 0
+  logfile: logs/f82baf39-3cf0-4dbc-b91e-5faa7d673347.txt
+  logit_softcap: 30.0
+  loop_end: 5
+  loop_start: 3
+  matrix_bits: 6
+  matrix_clip_sigmas: 12.85
+  matrix_lr: 0.022
+  max_wallclock_seconds: 600.0
+  min_lr: 0.0
+  mlp_mult: 4.0
+  model_dim: 512
+  model_path: final_model.pt
+  muon_backend_steps: 5
+  muon_beta2: 0.95
+  muon_momentum: 0.99
+  muon_momentum_warmup_start: 0.92
+  muon_momentum_warmup_steps: 1500
+  muon_row_normalize: True
+  muon_wd: 0.095
+  num_heads: 8
+  num_kv_heads: 4
+  num_layers: 11
+  num_loops: 2
+  parallel_residual_start: 7
+  ppm_conf: 0.9
+  ppm_enabled: False
+  ppm_lhi: 0.9
+  ppm_llo: 0.05
+  ppm_order: 5
+  qk_gain_init: 5.0
+  quantized_model_path: final_model.int6.ptz
+  rank: 0
+  rope_base: 10000.0
+  rope_dims: 16
+  rope_train_seq_len: 2048
+  run_id: f82baf39-3cf0-4dbc-b91e-5faa7d673347
+  scalar_lr: 0.02
+  seed: 2025
+  skip_gates_enabled: True
+  sliding_window_enabled: True
+  tie_embeddings: True
+  tied_embed_init_std: 0.005
+  tied_embed_lr: 0.03
+  tokenizer_path: ./data/tokenizers/fineweb_10240_casefold_bpe.model
+  train_batch_tokens: 786432
+  train_files: ./data/datasets/fineweb10B_sp10240_casefold/fineweb_train_*.bin
+  train_log_every: 500
+  train_seq_len: 2048
+  ttt_chunk_tokens: 32768
+  ttt_enabled: False
+  ttt_epochs: 3
+  ttt_lr: 0.005
+  ttt_momentum: 0.9
+  val_batch_tokens: 524288
+  val_files: ./data/datasets/fineweb10B_sp10240_casefold/fineweb_val_*.bin
+  val_loss_every: 4000
+  vocab_size: 10240
+  warmdown_frac: 0.72
+  warmup_steps: 20
+  world_size: 8
+  xsa_last_n: 11
+train_shards: 101
+val_tokens: 49999872
+model_params:36993112
+gptq:reserving 12s, effective=588000ms
+warmup_step: 1/20
+warmup_step: 2/20
+warmup_step: 3/20
+warmup_step: 4/20
+warmup_step: 5/20
+warmup_step: 6/20
+warmup_step: 10/20
+warmup_step: 20/20
+loop_warmup:enabled encoder:[0, 1, 2, 3, 4, 5, 3, 4] decoder:[5, 3, 4, 5, 6, 7, 8, 9, 10]
+loop_warmup_step: 1/20
+loop_warmup_step: 2/20
+loop_warmup_step: 3/20
+loop_warmup_step: 4/20
+loop_warmup_step: 5/20
+loop_warmup_step: 6/20
+loop_warmup_step: 10/20
+loop_warmup_step: 20/20
+0/20000 val_loss: 9.2318 val_bpb: 3.3642
+1/20000 train_loss: 9.2314 train_time: 0.0m tok/s: 8229714
+2/20000 train_loss: 12.3115 train_time: 0.0m tok/s: 7878685
+3/20000 train_loss: 10.8396 train_time: 0.0m tok/s: 7698793
+4/20000 train_loss: 9.3423 train_time: 0.0m tok/s: 7602015
+5/20000 train_loss: 8.6486 train_time: 0.0m tok/s: 7563774
+500/20000 train_loss: 3.4690 train_time: 0.9m tok/s: 7671440
+1000/20000 train_loss: 3.3554 train_time: 1.7m tok/s: 7673873
+1500/20000 train_loss: 3.3457 train_time: 2.6m tok/s: 7676804
+2000/20000 train_loss: 3.2937 train_time: 3.4m tok/s: 7678709
+layer_loop:enabled step:2010 frac:0.350 encoder:[0, 1, 2, 3, 4, 5, 3, 4] decoder:[5, 3, 4, 5, 6, 7, 8, 9, 10]
+2500/20000 train_loss: 3.0838 train_time: 4.7m tok/s: 7036761
+3000/20000 train_loss: 3.1027 train_time: 5.9m tok/s: 6657083
+3500/20000 train_loss: 3.0158 train_time: 7.2m tok/s: 6408686
+4000/20000 train_loss: 2.9015 train_time: 8.4m tok/s: 6211177
+4000/20000 val_loss: 3.0142 val_bpb: 1.0984
+4500/20000 train_loss: 2.9896 train_time: 9.7m tok/s: 6086551
+4544/20000 val_loss: 2.9529 val_bpb: 1.0761
+stopping_early: wallclock_cap train_time: 588065ms step: 4544/20000
+peak memory allocated: 39441 MiB reserved: 39552 MiB
+ema:applying EMA weights
+pre-quantization post-ema val_loss:2.94983245 val_bpb:1.07497210 eval_time:8604ms
+Serialized model: 137528185 bytes
+Code size: 67657 bytes
+GPTQ:collecting Hessians from calibration data...
+GPTQ:collected 67 Hessians in 12.8s
+Quantized weights:
+  gptq (int6): blocks.attn.c_k.weight, blocks.attn.c_q.weight, blocks.attn.c_v.weight, blocks.attn.proj.weight, blocks.mlp.fc.weight, blocks.mlp.proj.weight
+  gptq (int7): tok_emb.weight
+  passthrough (float16): blocks.attn.q_gain, blocks.attn_scale, blocks.mlp_scale, blocks.resid_mix, skip_gates, skip_weights
+Serialized model quantized+brotli: 15932332 bytes
+Total submission size quantized+brotli: 15999989 bytes
+quantized val_loss:2.98883943 val_bpb:1.08918695 eval_time:10727ms
+quantized_sliding_window val_loss:2.94552081 val_bpb:1.07340087 eval_time:114732ms