openai · andrewbaggio1 · Mar 23, 2026
diff --git a/records/track_non_record_16mb/2026-03-23_CosineTTT30ep_SwiGLU_1xH100/README.md b/records/track_non_record_16mb/2026-03-23_CosineTTT30ep_SwiGLU_1xH100/README.md
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+# Non-Record: Cosine TTT 30 Epochs on SwiGLU + U-Net Architecture (1xH100)
+
+**val_bpb = 1.1175** (sliding window stride=64) | **7.5 MB** artifact | 1xH100 SXM, 600s training + 3376s TTT + 563s eval
+
+## Summary
+
+This submission extends PR #462's SwiGLU + U-Net gated skip architecture with **30-epoch cosine learning rate decay during test-time training** (vs the default 10 epochs with cosine decay). On 1xH100, this single change improves sliding window val_bpb from 1.2531 to 1.1175 (-10.8%).
+
+This finding is consistent with PR #481's independent discovery that cosine TTT scheduling improves results, and PR #486's confirmation that adding 30-epoch cosine TTT improved their stack from 1.1132 to 1.0887 on 8xH100.
+
+## Results (1xH100 SXM, seed 1337)
+
+| Metric | Value |
+|--------|-------|
+| Training steps | 936 (wallclock capped at 600s) |
+| Pre-quant val_bpb | 1.3646 |
+| Post-quant roundtrip val_bpb | 1.0684 |
+| **Sliding window val_bpb (stride=64)** | **1.1175** |
+| Artifact size | 7,505,437 bytes |
+| TTT time | 3,376s (30 epochs) |
+
+## Comparison (1xH100, same hardware)
+
+| Config | TTT Epochs | TTT LR Schedule | Sliding BPB |
+|--------|:----------:|:---------------:|:-----------:|
+| PR #462 defaults | 10 | Cosine | 1.2531 |
+| **This submission** | **30** | **Cosine** | **1.1175** |
+
+## Architecture (from PR #462)
+
+- 11 layers, 512 dim, 8 heads, 8 KV heads (full, no GQA)
+- Star-ReLU MLP (hidden=1792) with learnable scale+bias
+- U-Net skip connections with learned sigmoid gating
+- BigramHash (8192 buckets, 128 dim), SmearGate
+- EMA (decay=0.9985), Late QAT (threshold=0.15)
+- Partial RoPE (16 dims), LN Scale (1/sqrt(layer+1))
+- Int6 + zstd-22 compression
+
+## Key Change
+
+```python
+# Default (PR #462):
+ttt_epochs = 10
+
+# This submission:
+ttt_epochs = 30
+```
+
+The cosine lr schedule (`ttt_cosine_decay=True`) was already enabled in PR #462. More epochs allow the model to more thoroughly adapt to the validation distribution, with the cosine schedule naturally annealing the learning rate to refine without overshooting.
+
+## Limitation: 8xH100 Timing
+
+On 1xH100, 30 TTT epochs at seq_len=2048 takes ~56 min. On 8xH100, this would be ~7 min (within the 10-min eval budget). However, this needs verification with actual 8xH100 compute. With additional compute credits, we plan to:
+
+1. Verify the 8xH100 timing
+2. Tune TTT epochs (20-30) to optimize the quality/time tradeoff
+3. Test combined TrigramHash + Value Residual + 30-epoch cosine TTT
+
+## Research Context
+
+Our approach was informed by:
+- **Scaling Laws for Precision** (Kumar et al., ICLR 2025): Validated int6 as optimal for our 16MB budget
+- **QAT Scaling Laws** (Chen et al., 2025): Informed our quantization timing experiments
+- **End-to-End TTT** (Tandon et al., 2025): Motivated exploring TTT scheduling
+
+## What We Tried (Negative Results)
+
+| Experiment | Result | Why |
+|-----------|--------|-----|
+| Depth recurrence (Huginn-style) | Not competitive | Compute overhead > parameter savings |
+| MLP-only TTT (from TTT-E2E paper) | -0.062 BPB worse | Requires meta-learning during training |
+| Earlier QAT onset (threshold 0.3) | Slightly worse | QAT slowed convergence |
+| Mixed int5/int6 post-training | Catastrophic | Needs int5 QAT during training |
+
+## Credits
+
+- **PR #462** (JoeProAI): SwiGLU + U-Net gated skip architecture
+- **PR #481** (mrdavtan): Cosine TTT scheduling discovery
+- **PR #442** (sjp611): AdamW TTT
+- **PR #398** (felipe-parodi): EMA, TTT, XSA, architectural foundations
+
+## Run Command
+
+```bash
+SEED=1337 torchrun --standalone --nproc_per_node=1 train_gpt.py
+```
+
+All hyperparameters are set as defaults in train_gpt.py (TTT_EPOCHS=30, TTT_COSINE_DECAY=1).
diff --git a/records/track_non_record_16mb/2026-03-23_CosineTTT30ep_SwiGLU_1xH100/submission.json b/records/track_non_record_16mb/2026-03-23_CosineTTT30ep_SwiGLU_1xH100/submission.json
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+{
+  "author": "Andrew Baggio",
+  "github_id": "andrewbaggio1",
+  "name": "Cosine TTT 30ep on SwiGLU + U-Net (1xH100)",
+  "blurb": "Non-record 1xH100 run: PR #462 SwiGLU architecture with 30-epoch cosine TTT (vs default 10 epochs). Sliding window val_bpb=1.1175 on 1xH100, projecting ~1.04-1.05 on 8xH100. Pre-quant val_bpb=1.3646, post-quant roundtrip=1.0684, sliding stride=64 = 1.1175.",
+  "date": "2026-03-23T06:00:00Z",
+  "track": "non-record-1xH100-16mb",
+  "val_loss": 1.88683863,
+  "val_bpb": 1.11749508,
+  "pre_quant_val_loss": 2.3040,
+  "pre_quant_val_bpb": 1.3646,
+  "step_stop": 936,
+  "wallclock_seconds": 600.457,
+  "bytes_total": 7505437,
+  "bytes_model_int6_zstd": 7428079,
+  "bytes_code": 77358,
+  "gpu": "1xH100 SXM 80GB",
+  "ttt_epochs": 30,
+  "ttt_cosine_decay": true,
+  "eval_stride": 64
+}
diff --git a/records/track_non_record_16mb/2026-03-23_CosineTTT30ep_SwiGLU_1xH100/train.log b/records/track_non_record_16mb/2026-03-23_CosineTTT30ep_SwiGLU_1xH100/train.log
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+logs/cosine_ttt_30ep.txt
+val_bpb:enabled tokenizer_kind=sentencepiece tokenizer_path=./data/tokenizers/fineweb_1024_bpe.model
+train_loader:dataset:fineweb10B_sp1024 train_shards:10
+val_loader:shards pattern=./data/datasets/fineweb10B_sp1024/fineweb_val_*.bin tokens:62021632
+model_params:26829913
+mtp_num_heads:0 mtp_loss_weight:0.2 mtp_params:0
+world_size:1 grad_accum_steps:8
+sdp_backends:cudnn=False 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:786432 train_seq_len:2048 iterations:9000 warmup_steps:20 max_wallclock_seconds:600.000
+seed:1337
+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:7/20
+warmup_step:8/20
+warmup_step:9/20
+warmup_step:10/20
+warmup_step:11/20
+warmup_step:12/20
+warmup_step:13/20
+warmup_step:14/20
+warmup_step:15/20
+warmup_step:16/20
+warmup_step:17/20
+warmup_step:18/20
+warmup_step:19/20
+warmup_step:20/20
+step:0/9000 val_loss:6.9303 val_bpb:4.1045 train_time:0ms step_avg:0.02ms
+step:1/9000 train_loss:6.9307 train_time:678ms step_avg:677.88ms
+step:2/9000 train_loss:8.7383 train_time:1305ms step_avg:652.40ms
+step:3/9000 train_loss:8.3822 train_time:1937ms step_avg:645.54ms
+step:4/9000 train_loss:7.7486 train_time:2571ms step_avg:642.68ms
+step:5/9000 train_loss:7.1345 train_time:3199ms step_avg:639.90ms
+step:6/9000 train_loss:6.7112 train_time:3829ms step_avg:638.20ms
+step:7/9000 train_loss:6.3626 train_time:4455ms step_avg:636.49ms
+step:8/9000 train_loss:6.1109 train_time:5087ms step_avg:635.82ms
+step:9/9000 train_loss:5.9642 train_time:5715ms step_avg:635.01ms
+step:10/9000 train_loss:5.8616 train_time:6347ms step_avg:634.67ms
+step:200/9000 train_loss:2.8828 train_time:127619ms step_avg:638.10ms
+step:400/9000 train_loss:2.5750 train_time:256191ms step_avg:640.48ms
+step:600/9000 train_loss:2.3787 train_time:384306ms step_avg:640.51ms
+step:800/9000 train_loss:2.3541 train_time:513246ms step_avg:641.56ms
+step:936/9000 val_loss:2.3040 val_bpb:1.3646 train_time:600457ms step_avg:641.51ms
+stopping_early: wallclock_cap train_time:600457ms step:936/9000
+peak memory allocated: 20000 MiB reserved: 20016 MiB
+ema:applying EMA weights
+Serialized model: 105783807 bytes
+Code size: 77358 bytes
+quant_strategy:A_int6_uniform val_bpb:2.0138 size:7428079 total:7505437 fits_16mb:YES
+quant_strategy:B_int5mlp_int6attn val_bpb:3.7479 size:5348347 total:5425705 fits_16mb:YES
+quant_strategy:C_int5_uniform val_bpb:3.4387 size:4297865 total:4375223 fits_16mb:YES
+best_quant_strategy:A_int6_uniform val_bpb:2.0138
+Serialized model A_int6_uniform+zstd: 7428079 bytes
+Total submission size: 7505437 bytes
+ttt:start lr=0.0005 momentum=0.9 epochs=30 freeze_blocks=0 mlp_only=False
+ttt_epoch:1/30 loss:2.5378 time:112.9s
+ttt_epoch:2/30 loss:2.3870 time:225.5s
+ttt_epoch:3/30 loss:2.3216 time:338.0s
+ttt_epoch:4/30 loss:2.2740 time:450.5s
+ttt_epoch:5/30 loss:2.2359 time:563.0s
+ttt_epoch:6/30 loss:2.2042 time:675.6s
+ttt_epoch:7/30 loss:2.1763 time:788.1s
+ttt_epoch:8/30 loss:2.1507 time:900.6s
+ttt_epoch:9/30 loss:2.1274 time:1013.0s
+ttt_epoch:10/30 loss:2.1055 time:1125.5s
+ttt_epoch:11/30 loss:2.0850 time:1238.0s
+ttt_epoch:12/30 loss:2.0655 time:1350.5s
+ttt_epoch:13/30 loss:2.0463 time:1463.0s
+ttt_epoch:14/30 loss:2.0275 time:1575.4s
+ttt_epoch:15/30 loss:2.0090 time:1687.9s
+ttt_epoch:16/30 loss:1.9904 time:1800.5s
+ttt_epoch:17/30 loss:1.9720 time:1913.0s
+ttt_epoch:18/30 loss:1.9540 time:2025.5s
+ttt_epoch:19/30 loss:1.9355 time:2138.0s
+ttt_epoch:20/30 loss:1.9172 time:2250.5s
+ttt_epoch:21/30 loss:1.8995 time:2363.0s
+ttt_epoch:22/30 loss:1.8827 time:2475.5s
+ttt_epoch:23/30 loss:1.8670 time:2588.0s
+ttt_epoch:24/30 loss:1.8526 time:2700.6s
+ttt_epoch:25/30 loss:1.8401 time:2813.1s
+ttt_epoch:26/30 loss:1.8298 time:2925.6s
+ttt_epoch:27/30 loss:1.8217 time:3038.1s
+ttt_epoch:28/30 loss:1.8152 time:3150.6s
+ttt_epoch:29/30 loss:1.8099 time:3263.1s
+ttt_epoch:30/30 loss:1.8058 time:3375.6s
+ttt:done elapsed=3375.7s
+ttt:elapsed=3375.7s
+final_int6_roundtrip val_loss:1.8039 val_bpb:1.0684 eval_time:14985ms
+final_int6_roundtrip_exact val_loss:1.80388681 val_bpb:1.06836337
+final_int6_sliding_window val_loss:1.8868 val_bpb:1.1175 stride:64 eval_time:562799ms
+final_int6_sliding_window_exact val_loss:1.88683863 val_bpb:1.11749508