Non-record: Parallel Residuals + Hessian-Aware SDClip (3-seed mean 1.08354 BPB)

records/track_10min_16mb/2026-04-06_SP8192_HessianSDClip_ProgressiveRecurrence/README.md

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+# Non-record: Parallel Residuals + Hessian-Aware SDClip (3-seed mean 1.08354 BPB)
+
+**val bpb: 1.08354** (3-seed mean, std=0.00050)
+
+Not a record. This is a small 3-seed experiment over [PR #1394](https://github.com/openai/parameter-golf/pull/1394) on my runs, but not enough evidence for a statistical claim — the seed count is too small for confidence. Posting because the changes are zero-cost, reproducible, and may be useful to others trying out different techniques. 
+
+| Seed | Steps | Pre-quant BPB | Post-quant BPB | **Sliding BPB** | Artifact |
+|-|-|-|-|-|-|
+| 1337 | 5178 | 1.08765 | 1.09959 | **1.08301** | 15,976,275 |
+| 42 | 5180 | 1.08816 | 1.10013 | **1.08363** | 15,978,439 |
+| 3141 | 5182 | 1.08872 | 1.10044 | **1.08399** | 15,979,649 |
+| **Mean** | | 1.08818 | 1.10005 | **1.08354** | 15,978,121 |
+
+## Changes
+
+Three zero-cost modifications on top of [PR #1394](https://github.com/openai/parameter-golf/pull/1394), adding zero extra parameters or bytes:
+
+### 1. Parallel Residuals (Layers 7+)
+
+GPT-J style parallel attention+MLP ([Wang & Komatsuzaki, 2021](https://github.com/kingoflolz/mesh-transformer-jax)) for the last 4 layers. Both attention and MLP read from the same input and their outputs are added in parallel:
+
+```
+# Parallel (layers 7-10):
+x_out = x + attn_scale * Attn(norm(x)) + mlp_scale * MLP(norm(x))
+
+# Sequential (layers 0-6, unchanged):
+h = x + attn_scale * Attn(norm(x))
+x_out = h + mlp_scale * MLP(norm(h))
+```
+
+I expected parallel residuals to reduce interference between attention and MLP during GPTQ calibration. Pre-quant BPB barely moved, but the quantization gap tightened across all 3 seeds, which made this the most useful change in practice.
+
+### 2. Hessian-Aware SDClip
+
+I used GPTQ's existing Hessian diagonal as a cheap importance signal to slightly modulate SDClip thresholds by row:
+
+$$c_i = k \cdot \sigma_i \cdot [1 + \lambda(r_i - 1)], \quad \lambda = 0.175$$
+
+where $\sigma_i$ is the standard deviation of row $i$ and $r_i$ is the row importance derived from Hessian-weighted magnitude. The effect is small but directionally useful at $\lambda = 0.175$; higher $\lambda$ hurt compression. I initially used $\lambda = 0.30$ but found $\lambda = 0.175$ is consistently better across seeds — both lower BPB and smaller artifact. Higher $\lambda$ reduces rounding error but increases entropy, which makes Brotli compression less effective.
+
+### 3. Progressive Recurrence
+
+Depth recurrence split into two phases: first loop enabled at 50% of training, second at 65%. The split points were not optimized — 50% matches the original and 65% was a single manual choice. Enabling both loops at once causes a sharper loss spike; splitting gives the model time to adapt to each additional pass before adding the next.
+
+## Hessian Analysis (Cross-Seed)
+
+Hessian diagnostics from 3 seeds, 67 matrices each:
+
+- **Group-level traces** (early/loop/mid/late blocks): $r=0.997$ across seeds
+- **Per-matrix traces**: $r=0.994$
+- **Per-row importance**: $r=0.12$ (noise)
+
+Importance hierarchy: early blocks (30x trace of late blocks) >> loop >> mid >> late. Per-row importance is too noisy to be a reliable signal, but group-level traces are very stable across seeds. This suggests per-group clip allocation could be a useful direction.
+
+## Future Directions
+
+Several ideas I'd like to explore with more compute time:
+
+- **Per-group clip allocation**: Non-uniform $k$ across layer groups, using the stable group-level trace hierarchy as a guide.
+- **Output-Hessian weighting**: Using backward-pass gradients for output-side row importance rather than input-side alone.
+- **More seeds**: 3 seeds is not enough for strong statistical claims. I'd want 5+ to be confident about the gap vs PR #1394.
+- **YAQA**: I like the idea of the paper ([arXiv:2505.22988](https://arxiv.org/abs/2505.22988)), but I couldn't get a working backward pass for it. I think maybe it could be adapted for the parameter golf problem in an interesting way. I also like the math in Mousse ([arXiv:2603.09697](https://arxiv.org/abs/2603.09697)), but exploiting curvature in small LMs seems tough.
+
+## Run Command
+
+```bash
+HESSIAN_CLIP_LAMBDA=0.175 LOOP_PHASE2_AT=0.65 PARALLEL_RESIDUAL_START=7 SEED=1337 \
+torchrun --standalone --nproc_per_node=8 train_gpt_sweep.py
+```
+
+## Requirements
+
+Flash Attention 3 (Hopper) required. SP8192 BPE tokenizer trained on FineWeb 10B (sentencepiece BPE, 8192 vocab).
+
+```bash
+pip install torch --index-url https://download.pytorch.org/whl/cu130
+pip install --no-cache-dir \
+  "https://download.pytorch.org/whl/cu130/flash_attn_3-3.0.0-cp39-abi3-manylinux_2_28_x86_64.whl"
+pip install -r requirements.txt
+```
+
+## Compliance (Track A — Fixed Predictor)
+
+- No TTT, SLOT, n-gram cache, or eval-time adaptation
+- GPTQ calibration within training budget
+- Standard autoregressive sliding-window eval (stride=64)
+
+
+## Credits
+
+Learned from and inspired by [PR #1394](https://github.com/openai/parameter-golf/pull/1394) (@clarkkev) — SDClip, depth recurrence, and GPTQ embedding quantization ideas. Parallel residuals from GPT-J ([Wang & Komatsuzaki, 2021](https://github.com/kingoflolz/mesh-transformer-jax)). Additional credits: PR #1204 (@msisovic, depth recurrence), PR #1217 (@bigbag, MuonEq-R), PR #1019 (@abaybektursun, previous SOTA).

records/track_10min_16mb/2026-04-06_SP8192_HessianSDClip_ProgressiveRecurrence/submission.json

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+{
+  "author": "Robby Sneiderman",
+  "github_id": "Robby955",
+  "name": "Non-record: Parallel Residuals + Hessian-Aware SDClip",
+  "blurb": "Three zero-cost modifications on PR #1394: GPT-J parallel residuals (layers 7+), Hessian-diagonal SDClip modulation (lambda=0.175), two-phase progressive recurrence. 3-seed mean 1.08354 BPB.",
+  "date": "2026-04-06T00:00:00Z",
+  "val_loss": 2.7995,
+  "val_bpb": 1.08354,
+  "val_bpb_std": 0.00050,
+  "seeds": [1337, 42, 3141],
+  "seed_results": {
+    "1337": {
+      "val_loss": 2.79749,
+      "val_bpb": 1.08301,
+      "artifact_bytes": 15976275,
+      "steps": 5178
+    },
+    "42": {
+      "val_loss": 2.79910,
+      "val_bpb": 1.08363,
+      "artifact_bytes": 15978439,
+      "steps": 5180
+    },
+    "3141": {
+      "val_loss": 2.80002,
+      "val_bpb": 1.08399,
+      "artifact_bytes": 15979649,
+      "steps": 5182
+    }
+  },
+  "hardware": "8xH100 80GB SXM",
+  "bytes_total": 15978121,
+  "based_on": "PR #1394 (@clarkkev)"
+}

records/track_10min_16mb/2026-04-06_SP8192_HessianSDClip_ProgressiveRecurrence/train.log

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+W0406 05:48:52.138000 4182076 torch/distributed/run.py:803] 
+W0406 05:48:52.138000 4182076 torch/distributed/run.py:803] *****************************************
+W0406 05:48:52.138000 4182076 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. 
+W0406 05:48:52.138000 4182076 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_sp8192
+  distributed: True
+  ema_decay: 0.997
+  embed_bits: 8
+  embed_clip_sigmas: 20.0
+  embed_lr: 0.6
+  embed_wd: 0.085
+  embedding_dim: 512
+  enable_looping_at: 0.5
+  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
+  hessian_clip_lambda: 0.175
+  is_main_process: True
+  iterations: 20000
+  ln_scale: True
+  local_rank: 0
+  logfile: logs/f3971278-d577-499b-8fde-755434809ba9.txt
+  logit_softcap: 30.0
+  loop_end: 5
+  loop_layer_bits: 0
+  loop_layer_clip_sigmas: 0.0
+  loop_phase2_at: 0.65
+  loop_start: 4
+  matrix_bits: 6
+  matrix_clip_sigmas: 12.85
+  matrix_lr: 0.02
+  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: 0.085
+  num_heads: 8
+  num_kv_heads: 4
+  num_layers: 11
+  num_loops: 2
+  parallel_residual_start: 7
+  qk_gain_init: 4.0
+  quantized_model_path: final_model.int6.ptz
+  rank: 0
+  rope_base: 10000.0
+  rope_dims: 16
+  rope_train_seq_len: 2048
+  run_id: f3971278-d577-499b-8fde-755434809ba9
+  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_8192_bpe.model
+  train_batch_tokens: 786432
+  train_files: ./data/datasets/fineweb10B_sp8192/fineweb_train_*.bin
+  train_log_every: 500
+  train_seq_len: 2048
+  ttt_chunk_tokens: 32768
+  ttt_enabled: False
+  ttt_entropy_high: 2.1
+  ttt_entropy_low: 1.75
+  ttt_epochs: 4
+  ttt_freeze_blocks: 2
+  ttt_lr: 0.0005
+  ttt_ns_steps: 3
+  untie_loop_mlps: False
+  val_batch_tokens: 524288
+  val_files: ./data/datasets/fineweb10B_sp8192/fineweb_val_*.bin
+  val_loss_every: 4000
+  vocab_size: 8192
+  warmdown_frac: 0.667
+  warmup_steps: 20
+  world_size: 8
+  xsa_last_n: 11
+train_shards: 128
+val_tokens: 40540160
+model_params:35943512
+hessian_clip: lambda=0.175
+parallel_residuals: ON (layers 7-10)
+progressive_recurrence: phase1=0.5 phase2=0.65
+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_phase1: encoder:[0, 1, 2, 3, 4, 5] decoder:[4, 5, 6, 7, 8, 9, 10]
+loop_warmup_p1_step: 1/20
+loop_warmup_p1_step: 2/20
+loop_warmup_p1_step: 3/20
+loop_warmup_p1_step: 4/20
+loop_warmup_p1_step: 5/20
+loop_warmup_p1_step: 6/20
+loop_warmup_p1_step: 10/20
+loop_warmup_p1_step: 20/20
+loop_warmup_phase2: encoder:[0, 1, 2, 3, 4, 5, 4] decoder:[5, 4, 5, 6, 7, 8, 9, 10]
+loop_warmup_p2_step: 1/20
+loop_warmup_p2_step: 2/20
+loop_warmup_p2_step: 3/20
+loop_warmup_p2_step: 4/20
+loop_warmup_p2_step: 5/20
+loop_warmup_p2_step: 6/20
+loop_warmup_p2_step: 10/20
+loop_warmup_p2_step: 20/20
+0/20000 val_loss: 9.0052 val_bpb: 3.4862
+1/20000 train_loss: 9.0086 train_time: 0.0m tok/s: 8101558 looping:False
+2/20000 train_loss: 12.1911 train_time: 0.0m tok/s: 8047236 looping:False
+3/20000 train_loss: 11.0242 train_time: 0.0m tok/s: 7763017 looping:False
+4/20000 train_loss: 9.5010 train_time: 0.0m tok/s: 7744899 looping:False
+5/20000 train_loss: 8.3911 train_time: 0.0m tok/s: 7764479 looping:False
+500/20000 train_loss: 3.3106 train_time: 0.8m tok/s: 7721577 looping:False
+1000/20000 train_loss: 3.2012 train_time: 1.7m tok/s: 7711891 looping:False
+1500/20000 train_loss: 3.1827 train_time: 2.5m tok/s: 7711479 looping:False
+2000/20000 train_loss: 2.9936 train_time: 3.4m tok/s: 7711845 looping:False
+2500/20000 train_loss: 3.0679 train_time: 4.2m tok/s: 7713114 looping:False
+layer_loop:phase1 step:2884 frac:0.500
+3000/20000 train_loss: 3.1068 train_time: 5.1m tok/s: 7668374 looping:True
+3500/20000 train_loss: 2.9483 train_time: 6.1m tok/s: 7510155 looping:True
+layer_loop:phase2 step:3634 frac:0.650
+4000/20000 train_loss: 2.9482 train_time: 7.2m tok/s: 7297673 looping:True
+4000/20000 val_loss: 2.9279 val_bpb: 1.1335
+4500/20000 train_loss: 2.8499 train_time: 8.3m tok/s: 7110716 looping:True
+5000/20000 train_loss: 2.8598 train_time: 9.4m tok/s: 6967320 looping:True
+5178/20000 val_loss: 2.8121 val_bpb: 1.0887
+stopping_early: wallclock_cap train_time: 588103ms step: 5178/20000
+peak memory allocated: 34604 MiB reserved: 34634 MiB
+ema:applying EMA weights
+pre-quantization post-ema val_loss:2.80947408 val_bpb:1.08764765 eval_time:6554ms
+Serialized model: 135426937 bytes
+Code size: 78688 bytes
+GPTQ:collecting Hessians from calibration data...
+GPTQ:collected 67 Hessians in 11.3s
+GPTQ:saved Hessian diagnostics to hessian_diagnostics.pt (67 matrices)
+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 (int8): 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: 15976275 bytes
+Total submission size quantized+brotli: 16054963 bytes
+quantized val_loss:2.84032998 val_bpb:1.09959307 eval_time:8134ms
+quantized_sliding_window val_loss:2.79749368 val_bpb:1.08300961 eval_time:82837ms

records/track_10min_16mb/2026-04-06_SP8192_HessianSDClip_ProgressiveRecurrence/train_gpt.py

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+exec(open(__file__.replace("train_gpt.py","train_gpt_decode.py")).read())