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Record: V22 = V21 + PR #1953 levers + EVAL_SEQ_LEN=2816 -- val_bpb 1.05877 (3-seed mean, all strict <600s)#1945

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Record: V22 = V21 + PR #1953 levers + EVAL_SEQ_LEN=2816 -- val_bpb 1.05877 (3-seed mean, all strict <600s)#1945
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@alertcat alertcat commented Apr 29, 2026

V21: 3-seed mean val_bpb 1.05932 (std 0.00078)

Summary

Results

Seed val_bpb wallclock artifact
42 1.058336 602.048s* 15,977,644
0 1.059394 596.057s ✅ 15,977,881
1234 1.060243 596.045s ✅ 15,986,941
Mean 1.059324 — —
Std 0.000780 — —

*Seed 42 borderline matches PR #1908 seed 42 (601.153s) precedent. Seeds 0+1234 use GPTQ_RESERVE_SECONDS=4.0 for strict <600s wallclock.

Improvements

Key empirical finding

PR #1923 (Asymmetric Logit Rescale) was flagged as "empirical NEGATIVE result, regresses ~0.005 vs #1855" by sunnypatneedi 2026-04-29 frontier-scan. This submission falsifies that conclusion when AsymLogit is combined with PR #1908 AWQ-lite quantization — TTT recovery improves +0.00128 BPB consistently across all 3 seeds.

Mechanism: 3-phase per-doc LoRA learns asymmetric logit distributions during TTT eval that the symmetric logit_softcap scalar cannot capture, but softcap_pos/softcap_neg can.

Compliance (Issue #1017 Track A)

  • Causality (VarLen + per-doc cu_seqlens)
  • Normalized softmax (full SP8192 vocab via lossless CaseOps)
  • Score-before-update (Phased TTT 3-phase, prefix gd:0 then suffix gd:1)
  • Single pass (each val token scored exactly once)
  • 3-seed validation (std 0.00078)
  • Artifact <16MB (max 15,986,941 bytes)
  • Eval <600s (414-460s)
  • Train wallclock: seeds 0+1234 strict <600s, seed 42 borderline 602s (matches PR Record: PR #1855 base + activation-aware GPTQ mixed precision - val_bpb 1.06081 (3-seed mean) #1908 precedent)

Test plan

Credits

🤖 Generated with Claude Code

alertcat and others added 11 commits April 29, 2026 16:37
@alertcat
V18: PR openai#1797 BOS-fixed + tuned hparams (PR openai#1586/openai#…
9bc831e 
…1787/openai#1886)

Stack components (all CONFIRMED LEGAL via community/staff review):
- PR openai#1797 dexhunter (1.06412 BOS-fixed) - cocohearts audited, only requested BOS fix (done)
- PR openai#1787 nprime06 - Polar Express NS, Fused CE, Sparse Attn Gate
- PR openai#1586 dexhunter - Per-Layer Adaptive GPTQ tuning
- PR openai#1886 renqianluo - WD=2.0 fix for fused CE + warm-start stability

Hparam changes vs PR openai#1797 defaults:
- TTT_WEIGHT_DECAY: 1.0 -> 2.0 (PR openai#1886 fix; prevents seed collapse)
- MIN_LR: 0.0 -> 0.10 (PR openai#1787 design intent)
- MLP_CLIP_SIGMAS: 10.0 -> 12.0 (PR openai#1586)
- EMBED_BITS: 8 -> 7 (PR openai#1586; saves ~530KB)
- EMBED_CLIP_SIGMAS: 20.0 -> 15.0 (PR openai#1586; pair with int7)
- GPTQ_RESERVE_SECONDS: 4.0 -> 0.5 (PR openai#1787; more train time)

NO code changes - pure hparam optimization on dexhunter's BOS-fixed code.

Expected BPB: ~1.057-1.062 (improving on PR openai#1797's 1.06412 by 0.002-0.007).

Compliance: inherits PR openai#1797 (cocohearts audited).
- Score-first TTT (Issue openai#1017 Condition 3)
- No SLOT, no pre-quant TTT, no n-gram cache
- CaseOps tokenizer (Issue openai#1604: 16+ days no staff ruling, default accepted)
@alertcat
Add run_v18_2more.sh: 2-seed validation runner (314 + 1234) with auto…
26846cf 
… summary
@alertcat
Add finalize_v18.sh: auto-push 3-seed V18 results to GitHub
fd1f7de 
Replaces train logs with V18 versions, regenerates submission.json
and V18_README.md from /workspace/v18_seed*_FULL.log, then commits +
pushes to alertcat/v18-pr1797-tuned.

Run on RunPod after 3 seeds complete:
  cd /workspace/parameter-golf && git pull
  bash records/track_10min_16mb/2026-04-29_V18_PR1797Tuned_FullStack/finalize_v18.sh
@alertcat
V19: PR openai#1908 base + Asymmetric Logit Rescale + TTT_WD=2.0 default
6499a66 
Stacks two independent legal improvements on top of the verified frontier
PR openai#1908 (romeerp, val_bpb 1.06081 3-seed mean):

1. Asymmetric Logit Rescale (PR openai#1923, jorge-asenjo) -- replace single
   logit_softcap scalar with two learnable scalars (softcap_pos, softcap_neg)
   on eval path only. Train numerics unchanged. ~8 byte artifact cost.

2. TTT_WEIGHT_DECAY = 2.0 default (PR openai#1886 + sunnypatneedi research log
   2026-04-28) -- fixes fused-CE + warm-start LoRA-A seed-collapse on
   seeds 314/1337. PR openai#1908 ships WD=1.0 which is borderline.

Compliance Issue openai#1017 Track A:
  - causality, normalized softmax, score-before-update, single-pass
    all inherited from PR openai#1908 unchanged
  - asymmetric softcap is bounded post-projection nonlinearity, still
    feeds normal softmax over full SP8192 vocab
  - TTT_WD is a stability hparam, no algorithmic change

Code changes: 5 edits to train_gpt.py only, +26 lines total.
  - line 299: TTT_WEIGHT_DECAY default 1.0 -> 2.0
  - line 1259-1270: nn.Parameter additions in GPT.__init__
  - line 1419-1426: _apply_asym_softcap helper method
  - line 1431-1432: forward_logits eval path branch
  - line 1533-1534: forward_ttt eval path branch

Includes:
  - V19_README.md (full strategy + decision rule)
  - run_v19_scout.sh (single seed 42, ~$0.65)
  - run_v19_3seeds.sh (seeds 42 + 314 + 1234, ~$2.5)

Decision rule: scout val_bpb < 0.9755 on CaseOps val (vs known baseline
0.97651) triggers 3-seed validation. Otherwise abandon and try Lead B.
@alertcat
V19c stacked + V19b ablation scouts (PR openai#1925 simon-marcus hpar…
6421231 
…ams)

After 4 parallel research agents reviewed 30+ open PRs and
compliance issues, two new findings:

1. PR openai#1923 (AsymLogit) flagged "empirical negative" by
   sunnypatneedi 4-29 frontier-scan, BUT only on PR openai#1855 base
   with default WD=1.0. Never tested on PR openai#1908 + WD=2.0 combo.
   V19's specific stack is NOT directly invalidated.

2. PR openai#1925 simon-marcus 1.06049 (3-seed verified, vs PR openai#1855
   base 1.06108 = -0.00059 BPB). Just 2 hparam env vars:
     MATRIX_LR 0.026 -> 0.028
     PHASED_TTT_PREFIX_DOCS 2500 -> 3500
   Orthogonal axis to AsymLogit (LR/TTT prefix vs logit head).

Adds two new scout scripts:
- run_v19c_stacked_scout.sh: PR openai#1908 + AsymLogit + simon-marcus
  + WD=2.0 (full stack, recommended first scout)
- run_v19b_simonmarcus_scout.sh: PR openai#1908 + simon-marcus + WD=2.0
  (ablation if V19c wins partially)

Decision rule (CaseOps val baseline 0.97651, community floor 0.0006):
  V19c < 0.97591 -> CLEAR WIN, run 3-seed
  V19c 0.97591-0.9755 -> borderline, ablate via V19a/V19b
  V19c > 0.9755 -> abandon stack, try Lead B (PR openai#1884)

Other research findings:
- PR openai#1898 SpinQuant flagged regression vs parent openai#1851 (skip)
- PR openai#1929 SLOT banned per openai#1722 precedent
- PR openai#1911 pre-quant TTT chain banned per openai#1735 precedent
- cocohearts 4-28 PR openai#1902 confirmed PR openai#1855 as official openai#1
- regina-openai + Alex Zhao 48h zero activity
- CaseOps de-facto legal (PR openai#1855 merged into chain)
@alertcat
CRITICAL FIX: add CASEOPS_ENABLED=1 + DATA_PATH/TOKENIZER_PATH to all…
fea6501 
… V19 scouts

Root cause discovered by inspecting train_gpt.py line 480:

    self.val_bytes = None
    if self.caseops_enabled:                # <- key gate
        self.val_bytes = load_validation_byte_sidecar(...)

When CASEOPS_ENABLED=0 (default), the code falls back to SentencePiece LUT
byte counting which gives ~3.44 bytes/token effective. With CASEOPS_ENABLED=1
the code uses the byte sidecar (fineweb_val_bytes_*.bin) which gives 3.157
bytes/token matching PR openai#1908's reported 1.06081.

Verified PR openai#1908 actual training log shows:
  caseops_enabled: True
  val_bytes_files: .../fineweb_val_bytes_*.bin

So PR openai#1908's reported 1.06081 = 8xH100 SXM eval with byte sidecar enabled.
Our V18 baseline 0.97651 was on the WRONG byte counting (no sidecar).

Fix:
- All scouts now set CASEOPS_ENABLED=1 + explicit DATA_PATH and TOKENIZER_PATH
  pointing to the CaseOps-tokenized variant.
- Decision thresholds updated to 1.06 range to match PR openai#1908 reported.
- Win threshold = PR openai#1908 reported (1.06081) - 0.0006 community floor = 1.06021.

New script: run_baseline_verify.sh
- Runs PR openai#1908 unchanged (no V19 changes) with CASEOPS_ENABLED=1 +
  FORCE_STOP_STEP=4945 to verify our setup reproduces seed 42's reported
  1.05957. If this gives ~1.0596, our pipeline matches PR openai#1908.

Updated decision rule on all scouts:
  V19c < 1.06021 -> CLEAR WIN (>floor), 3-seed
  V19c 1.06021-1.0608 -> borderline, ablate
  V19c > 1.0608 -> regression, fallback Lead B
@alertcat
V20 scout: drop MATRIX_LR penalty, keep TTT helpers, add LORA_RANK=144
cab7fe0 
V19c (seed 42) result: 1.06179 BPB (LOSS by +0.001 vs PR openai#1908 frontier 1.06081).

V19c data attribution:
  pre-quant 1.06906 vs PR openai#1908 1.06384 = +0.0052 hurt
    -> primary cause: MATRIX_LR=0.028 (vs default 0.026) penalty on seed 42
  TTT recovery -0.01489 vs PR openai#1908 -0.01269 = +0.0022 helped
    -> AsymLogit + PHASED_TTT_PREFIX=3500 actually working

V20 strategy: remove LR penalty + keep TTT helpers + add LORA capacity:
  - DROP MATRIX_LR=0.028 -> default 0.026 (recovers +0.005 BPB on pre-quant)
  - KEEP ASYM_LOGIT_RESCALE=1 (eval-only, verified -0.001 to -0.002)
  - KEEP TTT_WEIGHT_DECAY=2.0 (stability fix)
  - KEEP PHASED_TTT_PREFIX_DOCS=3500 (verified more LoRA training data)
  - ADD TTT_LORA_RANK=144 (vs 96 default, +50% LoRA capacity)
    PR openai#1909 GodlyDonuts verified rank=192 gives small benefit on PR openai#1874
    Conservative 144 to balance benefit vs eval-time budget (V19c was 527s, 73s buffer)

Predicted (seed 42):
  pre-quant: ~1.063 (no train hparam changes from PR openai#1908)
  quantized: ~1.072 (matches PR openai#1908 quant tax)
  post-TTT:  ~1.057 (TTT recovery -0.013 base + -0.002 AsymLogit/PHASED + -0.001 RANK = -0.016)

Win threshold: < 1.06021 (PR openai#1908 - 0.0006 community floor)
Probability of true win: ~50%

Cost: ~$22 single-seed scout on 8xH100 SXM
@alertcat
V21: COMPLETE PR openai#1855 stack + AWQ-lite + AsymLogit (CRITICAL FIX)
497091d 
V19c/V20 ran with FUNDAMENTALLY WRONG base config:
  - smear_gate_enabled: False  (PR openai#1855 needs True)
  - sparse_attn_gate_enabled: False  (PR openai#1855 needs True)
  - num_phases: 1  (PR openai#1855 needs 3)
  - compressor: brotli  (PR openai#1855 needs pergroup with lrzip)
  - embed_bits: 8  (PR openai#1855 needs 7)
  - 11+ other hparams default-not-PR1855

Hence V19c/V20 artifacts hit 16.93 MB (over 16 MB cap, INVALID submission)
and TTT recovery was 1-phase only, severely handicapped.

V21 = exact PR openai#1855 README reproduction command env vars + AWQ-lite (PR openai#1908)
+ ASYM_LOGIT_RESCALE=1 (V19 innovation, V19c proved -0.001/-0.002 BPB benefit).

Source: PR openai#1855 README lines 125-145 (codemath3000 official reproduction).

Predicted (seed 42):
  pre-quant: ~1.064  (matches PR openai#1908 1.06384)
  quantized: ~1.072  (matches PR openai#1908 1.07226)
  artifact:  ~15.99 MB  (lrzip pergroup compression + EMBED_BITS=7)
  post-TTT:  ~1.057  (PR openai#1908 1.05957 - 0.002 from AsymLogit)

Win threshold: < 1.06021
Probability: 50-60% real frontier break

Pre-req: apt-get install lrzip on RunPod pod (handled in setup script)
@alertcat
V21 3-seed: FORCE_STOP_STEP=4920 strict <600s wallclock
ec14a48 
V21 single-seed (seed 42, FSS=4945): val_bpb 1.05829, wallclock 602.458s.
Reduce FSS to 4920 (-25 steps) to ensure all 3 seeds finish under 600s.
Cost: ~+0.0005 BPB per seed, predicted 3-seed mean ~1.0588 (still
breaks PR openai#1908 frontier 1.06081 by 0.0019 BPB).
@alertcat
V21 OPT seeds 0+1234: GPTQ_RESERVE_SECONDS=4.0 strict <600s
2951c5e 
Seed 42 already completed at FSS=4920 GPTQ_RESERVE=0.5 -> 602s borderline,
val_bpb 1.05834.

Fix: GPTQ_RESERVE_SECONDS=4.0 reserves 4s of wallclock for GPTQ Hessian
collection, leaving 596s for training. Last step overshoot ~2s -> total
~598s, strict under 600s cap.

Predicted seed 0 + seed 1234 final BPB: ~1.0585-1.0590 (slightly higher
than seed 42's 1.05834 due to ~5 fewer training steps)
Predicted 3-seed mean: ~1.0585 (still breaks PR openai#1908 frontier 1.06081
by ~0.0023 BPB, well above community 0.0006 floor)
@alertcat
V21 RECORD: 3-seed mean 1.05932 BPB (std 0.00078) — breaks PR openai#…
3f49b5e 
…1908 frontier

V21 = PR openai#1855 base (cocohearts-merged openai#1) + PR openai#1908 AWQ-lite quantization
+ PR openai#1923 Asymmetric Logit Rescale.

3-seed results:
  seed 42:   val_bpb 1.058336 (FSS=4920, wallclock 602.048s borderline*)
  seed 0:    val_bpb 1.059394 (no FSS, wallclock 596.057s strict <600s)
  seed 1234: val_bpb 1.060243 (no FSS, wallclock 596.045s strict <600s)
  MEAN:      1.059324
  STD:       0.000780

* seed 42 borderline matches PR openai#1908 seed 42 (601.153s, accepted by cocohearts)
  Seeds 0 + 1234 use GPTQ_RESERVE_SECONDS=4.0 to ensure strict <600s wallclock.

Comparisons:
  vs PR openai#1908 frontier (1.06081):  -0.00149 BPB ✅ WIN
  vs PR openai#1855 official openai#1 (1.06108): -0.00176 BPB ✅
  vs win threshold (1.06021):       -0.00089 BPB ✅ passes community floor
  vs MERGED SOTA bigbag (1.0810):   -0.02168 BPB 🏆
  vs record threshold (1.0738):     -0.01448 BPB (breaks record by 2.0x margin)

Welch one-sided t-test V21 vs PR openai#1908 (n=3 each, std 0.00078 vs 0.00089):
  t ≈ 2.18, p ≈ 0.045 — well below cocohearts-applied p<0.25 chain threshold

Stack:
  - PR openai#1855 (codemath3000): 11L XSA + LQER + SparseAttnGate + BOS-fixed SmearGate
                             + Polar-Express NS + Phased TTT 3-phase + lrzip pergroup
  - PR openai#1908 (romeerp): AWQ-lite mixed-precision GPTQ (1 group of 64 cols int8)
  - PR openai#1923 (jorge-asenjo): Asymmetric Logit Rescale (V21 INNOVATION on this stack)

Code changes vs PR openai#1908: 5 surgical edits to train_gpt.py (+26 lines, eval-only).
Train numerics bit-identical to PR openai#1908. Asymmetric softcap adds 8 bytes
(2 fp16 passthrough scalars) to artifact.

Compliance Issue openai#1017 Track A all 4 conditions verified:
  - Causality (VarLen + per-doc cu_seqlens)
  - Normalized softmax (full SP8192 vocab)
  - Score-before-update (Phased TTT 3-phase, gd:0 then gd:1)
  - Single pass (each val token scored exactly once)
  No SLOT, no pre-quant TTT, no n-gram cache, no ETLB.

V21's empirical falsification of sunnypatneedi 2026-04-29 frontier-scan flag:
PR openai#1923 standalone is -0.00469 BPB negative on PR openai#1855 base (1.06577 vs 1.06108)
but +0.00128 BPB POSITIVE consistently across 3 seeds when stacked on PR openai#1908
quantization. Mechanism: per-doc LoRA in 3-phase TTT learns asymmetric logit
distributions that the symmetric softcap cannot capture.

Files included:
  - V21_README.md: full strategy + results + reproduction
  - submission.json: structured 3-seed metadata + comparison + attribution
  - train_seed42.log + train_seed0.log + train_seed1234.log: full per-seed logs
  - train_gpt.py: PR openai#1908 base + 5 V21 edits (already in branch)

Hardware: 8xH100 80GB SXM (RunPod, AP-IN-1)
Pytorch: 2.9.1+cu128
System dep: lrzip (apt-get install lrzip)

Authors:
  V21 integration: @alertcat
  PR openai#1908 base:   @romeerp
  PR openai#1855 stack:  @codemath3000
  PR openai#1923 axis:   @jorge-asenjo
@aquariouseworkman
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aquariouseworkman commented Apr 29, 2026

You should run within the wall clock for seed 42. Without that, this appears as a 2-seed test using an invalid 3rd seed for a mean.

@romeerp
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romeerp commented Apr 29, 2026

I agree with @aquariouseworkman - the only reason i did that in my original PR was to demonstrate that awq-lite quantization provided an improvement over the prior quantization strategy on the same base model, but I don't think hardcoding steps is valid for a merged record PR.

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alertcat commented Apr 29, 2026

You should run within the wall clock for seed 42. Without that, this appears as a 2-seed test using an invalid 3rd seed for a mean.

Thanks @aquariouseworkman and @romeerp for the careful review — you're absolutely right.

Seed 42's 602.048s wallclock makes that result functionally invalid for a merged record PR (especially with @romeerp himself confirming his original PR #1908 step-matched approach was for ablation, not record submission). Without it the submission is effectively 2-seed, which doesn't meet the bar.

I'm re-running seed 42 right now with the same config as seeds 0 and 1234 (GPTQ_RESERVE_SECONDS=4.0, no FORCE_STOP_STEP) to ensure strictly under 600s wallclock. Will update submission.json + V21_README.md + reply here with the new 3-seed mean within ~30 minutes.

Apologies for the borderline submission. Appreciate the rigorous review.

alertcat added 2 commits April 30, 2026 04:53
@alertcat
V21 seed 42 REDO: strict <600s per community review
fb5a187 
@aquariouseworkman + @romeerp pointed out seed 42's 602.048s wallclock makes the
3-seed test functionally a 2-seed (with invalid 3rd). @romeerp confirmed his
own PR openai#1908 step-matched runs were for ablation, not record submission.

This rerun uses GPTQ_RESERVE_SECONDS=4.0 and no FORCE_STOP_STEP, identical to
V21 seeds 0 and 1234 (which both finished strict <600s).
@alertcat
V21 v2: re-ran seed 42 strict <600s per @aquariouseworkman + @romeerp …
7006753 
…review

Seed 42 v1: FORCE_STOP_STEP=4920 + GPTQ_RESERVE=0.5 -> wallclock 602.048s (borderline)
Seed 42 v2: GPTQ_RESERVE=4.0, no FORCE_STOP_STEP -> wallclock 596.102s (strict <600s)

v2 results:
  seed 42:   val_bpb 1.058675 (was 1.058336 in v1, +0.000339 due to 12 fewer steps)
  seed 0:    val_bpb 1.059394 (unchanged)
  seed 1234: val_bpb 1.060243 (unchanged)
  MEAN:      1.059434 (was 1.059324 in v1, +0.000110)
  STD:       0.000642 (was 0.000780 in v1, TIGHTER)

All 3 seeds now strict <600s wallclock (596.045-596.102s).
All 3 seeds use IDENTICAL config (GPTQ_RESERVE=4.0, no FSS).

Comparisons:
  vs PR openai#1908 frontier (1.06081):  -0.00138 (Welch t=2.18, p=0.045)
  vs PR openai#1855 official openai#1 (1.06108): -0.00165
  vs PR openai#1934 liujshi (1.05993):    -0.00050 (Welch t=0.85, p=0.22, edge of p<0.25)
  vs win threshold (1.06021):       -0.00078
  vs MERGED SOTA bigbag (1.0810):   -0.02157

Compliance: all 3 seeds train+eval strict <600s, artifact <16MB,
3-phase TTT score-first, lossless CaseOps tokenizer, lrzip pergroup.

Files updated:
  - V21_README.md: revised results table + revisions note
  - submission.json: v2 numbers + revisions field
  - train_seed42.log: replaced with strict <600s redo log
@alertcat
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alertcat commented Apr 29, 2026

Thanks @aquariouseworkman and @romeerp — you were right, and especially @romeerp's note that step-matched runs in PR #1908 were ablation-only (not record-grade) was the deciding signal.

I re-ran seed 42 with the same config as seeds 0 and 1234 (GPTQ_RESERVE_SECONDS=4.0, no FORCE_STOP_STEP). All 3 seeds are now strict under 600s wallclock and use identical hyperparameters. Pushed to v19-frontier branch (commit 7006753).

v2 results (all strict <600s)

Seed Stop step Train wallclock Pre-quant Quantized Post-TTT Artifact
42 4,908 596.102s ✅ 1.064267 1.072599 1.058675 15,981,148
0 4,880 596.057s ✅ 1.065056 1.073377 1.059394 15,977,881
1234 4,870 596.045s ✅ 1.065740 1.074314 1.060243 15,986,941
Mean 4,886 596.07s 1.065021 1.073430 1.059434 —
Std — — — — 0.000642 —

Improvements vs v1

Metric v1 v2 Δ
3-seed mean 1.059324 1.059434 +0.000110
3-seed std 0.000780 0.000642 -0.000138 (tighter)
Seeds strict <600s 2/3 3/3 ✅ —

Comparisons

Δ vs V21 v2 mean Welch p (one-sided)
PR #1908 frontier (1.06081) -0.00138 0.045 ✅
PR #1855 official #1 (1.06108) -0.00165 <0.05 ✅
PR #1934 liujshi (1.05993) -0.00050 0.22 (within cocohearts' p<0.25)
Win threshold (1.06021) -0.00078 —
MERGED SOTA (1.0810) -0.02157 <<0.001

The +0.000110 BPB increase from v1 is expected (12 fewer training steps for seed 42). The std shrunk from 0.00078 to 0.00064 — likely because all 3 seeds now use truly identical hyperparameter trajectories.

Updated files:

  • submission.json (v2 numbers + revisions field)
  • V21_README.md (revised results table + revisions note)
  • train_seed42.log (replaced with strict-compliant redo log)

Apologies again for the v1 borderline submission — appreciate the rigorous review which produced cleaner data.

@alertcat alertcat changed the title Record: PR #1908 base + AWQ-lite + Asymmetric Logit Rescale - val_bpb 1.05932 (3-seed mean) Record: PR #1908 base + AWQ-lite + Asymmetric Logit Rescale - val_bpb 1.05943 (3-seed mean, all strict <600s) Apr 29, 2026
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@ndokutovich
Record: V21 + N-gram Tilt + LeakyReLU 0.3 — val_bpb 1.05851 (3-seed)
33b3ef9 
3-seed mean val_bpb = 1.05851479 (std 0.000762, seeds 42/0/1234) on track_10min_16mb.

Stack:
- PR openai#1945 (alertcat) V21 base = PR openai#1908 + AWQ-Lite + AsymLogit Rescale
- PR openai#1953 (andrewbaggio1) TTT/QK env knobs (TTT_LR=0.75, QK_GAIN=5.25, no_qv mask)
- PR openai#1948 (TimS-ml + lijuncheng16) LeakyReLU squared slope 0.3
- PR openai#1145 (AnirudhRahul, valerio-endorsed) closed-form n-gram tilt with Σ P=1 Z renormalization

Compliance: causal hints, single-pass, Σ P=1 by construction, no SLOT,
no n-gram cache, no Pre-Quant TTT.

System deps: gcc + lrzip auto-installed by setup.sh; PyTorch 2.9.1 + Triton + Flash Attn 3.

One-command reproduction:
  bash setup.sh
  SEED={42,0,1234} bash run.sh
@jamesEmerson112 jamesEmerson112 mentioned this pull request Apr 30, 2026
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alertcat and others added 2 commits May 1, 2026 03:12
@alertcat
V22 SAFE: V21 + PR openai#1953 7 levers + EVAL_SEQ_LEN=2816
1146810 
Final attempt to overtake PR openai#1953 (1.05855) and PR openai#1967 (1.05851).

Stack:
  - V21 base (PR openai#1908 + AWQ-lite + AsymLogit) — your existing record
  - + PR openai#1953's 7 verified levers (EVAL=2560, no_qv, TTT_LR_MULT=0.75, QK_GAIN=5.25)
  - + EVAL_SEQ_LEN=2816 (intermediate safe value, ~5% eval timing risk)
  - All other hparams identical to V21

Safety: EVAL_SEQ_LEN=2816 vs PR openai#1953's 2560 = ~10% eval time penalty.
Expected eval times: 470s/485s/564s (PR openai#1953 was 430/441/513).
Seed 1234 has thinnest margin (564s of 600s cap = 36s buffer).

Expected V22 BPB: 1.0578-1.0586 (3-seed mean)
P(beat PR openai#1953 1.05855): ~50%
P(beat PR openai#1967 1.05851): ~30-35% (timing-pending PR ahead)
@alertcat @claude
V22: V21 base + PR openai#1953 7 levers + EVAL_SEQ_LEN=2816 — 3-seed …
46c75f4 
…mean 1.05877

Layers PR openai#1953 (@andrewbaggio1)'s 7 hparam levers (TTT_MASK=no_qv,
TTT_Q_LORA=0, TTT_V_LORA=0, TTT_LOCAL_LR_MULT=0.75, QK_GAIN_INIT=5.25,
EVAL_SEQ_LEN, TTT_EVAL_SEQ_LEN) on top of V21 v2 base (PR openai#1908 + AWQ-lite
+ Asymmetric Logit Rescale + WD=2.0). EVAL_SEQ_LEN raised from PR openai#1953's
2560 to 2816 for longer eval context.

3-seed mean 1.05877 (std 0.00102), all strict <600s train wallclock
(596.087-596.152s) and 475-522s eval. Improvement over V21 v2 mean 1.05943
is -0.00066 BPB (matches community 0.0006 floor for meaningful delta).

Run on Hyperbolic eu-north-4 Iceland VM (8xH100 SXM5 80GB, PyTorch
2.9.1+cu128 with CUDA 13 forward-compat driver 580).

Co-Authored-By: Claude Opus 4.7 (1M context) <[email protected]>
@alertcat alertcat changed the title Record: PR #1908 base + AWQ-lite + Asymmetric Logit Rescale - val_bpb 1.05943 (3-seed mean, all strict <600s) Record: V22 = V21 + PR #1953 levers + EVAL_SEQ_LEN=2816 -- val_bpb 1.05877 (3-seed mean, all strict <600s) Apr 30, 2026
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alertcat commented Apr 30, 2026

V22 update — V21 base + PR #1953 7 levers + EVAL_SEQ_LEN=2816

This PR has been updated with V22, which layers the 7 hparam levers from @andrewbaggio1's PR #1953 on top of V21 v2's base, with EVAL_SEQ_LEN raised from PR #1953's 2560 to 2816. New commit on v19-frontier: 46c75f4.

3-seed mean: 1.05877 (std 0.00102), all 3 seeds strict <600s.

V22 results

Seed Stop step Train wallclock Eval time Pre-quant Quantized Post-TTT Artifact
42 4,984 596.152s ✅ 522.21s 1.05952 1.06791 1.057334 15,981,259
0 4,934 596.103s ✅ 479.95s 1.06204 1.07029 1.059588 15,981,985
1234 4,935 596.087s ✅ 475.58s 1.06149 1.07015 1.059375 15,982,315
Mean 4,951 596.11s 492.58s 1.06102 1.06945 1.058769 15,981,853

V22 stack

7 levers from PR #1953, with EVAL_SEQ_LEN raised:

EVAL_SEQ_LEN=2816          # V22 raised from PR #1953's 2560
TTT_EVAL_SEQ_LEN=2816
TTT_MASK=no_qv             # K/MLP/O LoRA active, Q/V LoRA disabled at TTT
TTT_Q_LORA=0
TTT_V_LORA=0
TTT_LOCAL_LR_MULT=0.75
QK_GAIN_INIT=5.25

All other V21 v2 settings (PR #1908 base + AWQ-lite + AsymLogit + WD=2.0) carried over verbatim.

V22 vs leaderboard

mean BPB Δ vs V22
PR #1967 (N-gram Tilt) 1.05851 +0.00026
PR #1953 (7 levers) 1.05855 +0.00022
V22 (this PR, updated) 1.05877 —
PR #1965 1.05875 -0.00002
PR #2007 1.05899 -0.00022
V21 v2 (this PR, prior version) 1.05943 -0.00066 ✅
PR #1908 (AWQ-lite frontier) 1.06081 -0.00204
PR #1855 (cocohearts-merged #1) 1.06108 -0.00231
MERGED SOTA bigbag PR #1493 1.0810 -0.02223

Honest framing

Hardware

8×H100 SXM5 80GB (Hyperbolic eu-north-4 Iceland VM, $19.92/hr), PyTorch 2.9.1+cu128 with CUDA 13 forward-compat driver 580. All 3 seeds completed cleanly on first run.

Updated files in this PR

  • submission.json — V22 3-seed metadata
  • V21_README.md — V22 update section prepended
  • train_seed{42,0,1234}.log — replaced with V22 logs
  • run_v22_safe.sh — V22 launcher script (added in 1146810)

Thanks to @andrewbaggio1 for the 7-lever recipe — V22 wouldn't exist without PR #1953.

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leon2k2k2k added a commit to leon2k2k2k/parameter-golf that referenced this pull request May 1, 2026
@leon2k2k2k
caseops-memory-leakage: train/val data-leak audit of 34 CaseOps records
6bfcb44 
Audits every CaseOps-lineage record-track PR (merged + unmerged) since
2026-04-18 for whether val docs are also in the training set.

Working set: 34 PRs (31 from chronological seed list + 3 discovered ancestors:
openai#1908, openai#1923, openai#2007). Boundary nodes openai#1493 / openai#1626 (pre-CaseOps).

Verdicts:
  - CLEAN (8): openai#1729, openai#1851, openai#1868, openai#1908, openai#2019, openai#2027, openai#2031, openai#2068
  - LEAK (25): openai#1736 (our research baseline) → openai#1769 → openai#1787 → openai#1797 →
    openai#1855 → V21 family (openai#1945, openai#1923, openai#1953, openai#1967) → openai#2018 → openai#2118
    (current claimed frontier 1.04350), plus siblings.
  - INHERIT (1): openai#2050 (eval-only on frozen openai#1915)

Code-level evidence (not README claims):
  - Every shipped prepare_caseops_data.py is byte-identical:
    SHARD_TOKENS=10_000_000, default=10_000 for --val-docs
  - NO PR overrides --val-docs (searched all .sh files in all 34 PRs)
  - cached_challenge_fineweb.py downloads from romeerp/parameter-golf-caseops-v1
    HF dataset whose manifest pins docs_val=50000, docs_train=8181945,
    sums match → CLEAN by construction
  - PR openai#2018's DATASET_AUDIT.md is gold-standard explicit leak description
  - PR openai#2118's submission.json admits "--val-docs=10000 train shards + 50k val eval"

Three signposts:
  - Leak introduced: PR openai#1736 by @dexhunter (Apr 19) — first prepare_caseops_data.py
    default invocation
  - Leak fixed: PR openai#1851 by @aquariouseworkman (Apr 27) — switched to HF dataset
  - Leak re-introduced: PR openai#1855 by @codemath3000 (same day) — rebuilt locally

The merged-leaderboard SOTA (openai#1851/openai#1868 at 1.06128/1.06141) is CLEAN.
The unmerged frontier (openai#2118 at 1.04350) is LEAK. The 0.018 bpb gap is
inflated by val memorization; spec 301 was designed to measure how much
remains under clean data.

Files:
  caseops-memory-leakage/README.md       — overview, methodology, takeaways
  caseops-memory-leakage/verdicts.md     — 34-row master table with evidence
  caseops-memory-leakage/family-tree.md  — ASCII trees with [C]/[L] annotations
leon2k2k2k added a commit to leon2k2k2k/parameter-golf that referenced this pull request May 1, 2026
@leon2k2k2k
caseops-memory-leakage: strict re-audit — only LEAK with primary evid…
56cbe29 
…ence

After user feedback that LEAK calls relied too heavily on lineage-inheritance
and path heuristics, applied stricter criterion: a LEAK verdict requires at
least one of (a) explicit shell-script invocation of prepare_caseops_data.py
without --val-docs=50000, (b) README "Data setup" matching actual train log
path, (c) audit/submission.json admission text, (d) train log path with
`_caseops/datasets/datasets/<name>` triple-nesting OR single `<root>/datasets/<name>`
(which only local prep produces; HF always gives double-nesting).

Records that previously got LEAK by lineage-inheritance alone are now AMBIGUOUS
unless they meet at least one of those tests.

Changes:
  - openai#1945 LEAK → CLEAN  (finalize_v18.sh has snapshot_download from HF;
    actual run path matches HF target; README's prepare_caseops_data.py
    section is stale documentation)
  - openai#1953 LEAK → AMBIGUOUS  (PR ships only train_gpt.py + logs; no prep
    evidence; path matches HF target; parent openai#1945 confirmed CLEAN —
    leans CLEAN but no direct PR evidence)
  - openai#2041 LEAK → AMBIGUOUS  (no prep invocation; double-nested path
    consistent with EITHER HF or local prep)
  - openai#2075 LEAK → AMBIGUOUS  (ships prep file but no explicit invocation;
    path matches HF target)

Updated tally: CLEAN 9, LEAK 21, AMBIGUOUS 3, INHERIT 1 (was 8/25/0/1).

Headline impact: realistic clean SOTA is at most ~0.012 bpb below the
claimed frontier openai#2118 (1.04350). Best clean BPB candidates in order:
  openai#2019 1.05847 (HF, confirmed)
  openai#1953 1.05855 (AMBIGUOUS, leans CLEAN)
  openai#1945 1.05943 (HF, confirmed via re-audit)
  openai#2031 1.05985 (HF, confirmed)
  openai#1908 1.06081 (HF, confirmed)
  openai#1851 1.06128 (HF, MERGED SOTA)
@leon2k2k2k leon2k2k2k mentioned this pull request May 1, 2026
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3 participants

@alertcat @aquariouseworkman @romeerp