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Non-record: Value Residual (-0.015 BPB) + Gated Attention (-0.003 BPB) with ablations#413

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Non-record: Value Residual (-0.015 BPB) + Gated Attention (-0.003 BPB) with ablations#413
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Non-record: Value Residual (-0.015 BPB) + Gated Attention (-0.003 BPB) with ablations

val_bpb: 1.4525 (sliding window, stride=128, GA+VR combined) | 13.2 MB | 1xRTX3090, 1000 steps, 131K batch

Two novel architecture modifications and one negative result. Sharing validated techniques with controlled ablation data.

Contributions

  1. Value Residual (ResFormer) -- -0.015 BPB. Cache V vectors from layer 0, mix into all subsequent layers via learnable scalars. 18 params total. arXiv:2410.17897 (ACL 2025). Enable: VALUE_RESIDUAL=1.

  2. Gated Attention -- -0.003 BPB. Per-head sigmoid gate after SDPA output, eliminating attention sinks. ~37K params. arXiv:2505.06708 (NeurIPS 2025 Best Paper). Enable: GATED_ATTENTION=1.

  3. PPM-C Context Mixer -- +0.0018 BPB (negative result). Classical compression blended with neural softmax. Dilutes predictions on SmearGate+BigramHash models.

The two positive techniques stack additively for -0.017 BPB combined.

Ablation Results

v1024 9L 2xMLP, SmearGate + BigramHash + OrthoInit + WD 0.04, 131K batch, 1000 steps.

Config Sliding BPB Delta vs Control
Control 1.4697 --
Gated Attention only 1.4665 -0.0032
Value Residual only 1.4546 -0.0151
GA + VR combined 1.4525 -0.0172
PPM-C (eval-only) 1.2900 +0.0018 (worse)

A production run (11L MLP3x + full community stack + VR + GA, 9500 steps) is in progress. Results in a follow-up submission if competitive.

Files

  • README.md -- Full writeup with technique details and reproducibility
  • submission.json -- Metadata
  • train_gpt.py -- Training script with Value Residual, Gated Attention, XSA, EMA, Partial RoPE, LN Scale

…) with ablations

Two novel architecture modifications validated with controlled ablations:
- Value Residual: layer-0 V shortcut, 18 scalars, -0.015 BPB
- Gated Attention: per-head sigmoid gate, -0.003 BPB
- PPM-C: negative result (+0.002 BPB on SmearGate+BigramHash)
Combined: -0.017 BPB additive, no interference.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
joshuaswarren added a commit to joshuaswarren/parameter-golf that referenced this pull request Mar 22, 2026
…+ Gated Attention + BigramHash(10240) + 12L (val_bpb=1.1690)

First submission combining 6 independently-proven architecture improvements:
- Catalytic Residuals (PR openai#450, -0.024 bpb)
- Value Residual/ResFormer (PR openai#413, -0.015 bpb)
- Gated Attention (PR openai#413, -0.003 bpb)
- BigramHash(10240) (PR openai#450, -0.070 bpb vs 2048)
- 12 Layers (-0.023 bpb vs 11L)
- 3x MLP

8xH100 SXM: 6981 steps, 85.78ms/step, 15.3MB artifact (int6+zstd)
@MatoTeziTanka
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Sibling Draft Review for PR #413

Date: 2026-04-12
Reviewer: Claude Agent (for Mato/@MatoTeziTanka)
NEEDS_LLM_REVIEW: Yes
Status: Ready for detailed code review

PR Summary

Title: Non-record: Value Residual (-0.015 BPB) + Gated Attention (-0.003 BPB) with ablations
State: OPEN
Mato blocking comment: NO

Marker Analysis

Marker Found Notes
target_in_key False Custom loss key injection pattern
TTT True Test-Time Training integration
SLOT False Slot-based attention variant
custom_tokenizer True 3 patterns detected

Architecture Changes

train_gpt.py modifications: ~1659 lines changed
Quantization: True
EMA: True
LoRA: False
GPTQ: False

Assessment

⚠ REQUIRES_REVIEW - Custom modifications detected:

  • TTT (Test-Time Training) integration detected
  • Custom tokenizer patterns found (3 instances)

Recommendation

REVIEW — Detailed code inspection needed before merge

Review Checklist

  • Code style and formatting (PEP 8, consistency)
  • Numerical stability of modifications
  • Integration with existing pipeline
  • Reproducibility (seed handling, RNG control)
  • Performance metrics reported correctly
  • No undocumented dependencies

Next Steps

  1. Full train_gpt.py code review
  2. Validate loss computation and gradients
  3. Check for integration issues with existing pipeline
  4. Verify metrics reproducibility

Generated for: Mato (@MatoTeziTanka)
Scope: NEEDS_LLM_REVIEW sweep
Action: Draft only—no posts to GitHub

Recommendation to @cocohearts @valerio-oai @0hq @yuzhougu-oai @notapplica: MERGE-pending standard record-track checks.


Reviewed by @MatoTeziTankaThe Agora. Classification via sibling-session agent (Haiku-backed). This review was drafted from a template and spot-checked before posting — if the template misread your code, please call it out so I can iterate the classifier.

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@MatoTeziTanka
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Sibling Draft Review for PR #413

Date: 2026-04-12
Reviewer: Claude Agent (for Mato/@MatoTeziTanka)
NEEDS_LLM_REVIEW: Yes
Status: Ready for detailed code review

PR Summary

Title: Non-record: Value Residual (-0.015 BPB) + Gated Attention (-0.003 BPB) with ablations
State: OPEN
Mato blocking comment: NO

Marker Analysis

Marker Found Notes
target_in_key False Custom loss key injection pattern
TTT True Test-Time Training integration
SLOT False Slot-based attention variant
custom_tokenizer True 3 patterns detected

Architecture Changes

train_gpt.py modifications: ~1659 lines changed
Quantization: True
EMA: True
LoRA: False
GPTQ: False

Assessment

⚠ REQUIRES_REVIEW - Custom modifications detected:

  • TTT (Test-Time Training) integration detected
  • Custom tokenizer patterns found (3 instances)

Recommendation

REVIEW — Detailed code inspection needed before merge

Review Checklist

  • Code style and formatting (PEP 8, consistency)
  • Numerical stability of modifications
  • Integration with existing pipeline
  • Reproducibility (seed handling, RNG control)
  • Performance metrics reported correctly
  • No undocumented dependencies

Next Steps

  1. Full train_gpt.py code review
  2. Validate loss computation and gradients
  3. Check for integration issues with existing pipeline
  4. Verify metrics reproducibility

Generated for: Mato (@MatoTeziTanka)
Scope: NEEDS_LLM_REVIEW sweep
Action: Draft only—no posts to GitHub

Recommendation to @cocohearts @valerio-oai @0hq @yuzhougu-oai @notapplica: MERGE-pending standard record-track checks.


Reviewed by @MatoTeziTankaThe Agora. Classification via sibling-session agent (Haiku-backed). This review was drafted from a template and spot-checked before posting — if the template misread your code, please call it out so I can iterate the classifier.

@MatoTeziTanka
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Community Review — Non-record: Value Residual (-0.015 BPB) + Gated Attention (-0.003 BPB) with ablations

Compliance flag: Pre-Quant TTT violation (multi-epoch SGD on val_tokens before scoring)

PR 413 — Non-record: Value Residual + Gated Attention (anantdgoel)

Head SHA: c0a2939
Track: non-record-16mb
Reported val_bpb: 1.4525 (pre_quant_val_bpb: 1.4525)
Hardware: 1×RTX3090 (ablations), 1×A6000 (validation)


Check 1: N-gram Family Bug (target token in hash key)

CLEAN. The BigramHash at line 930–936 computes:

out[..., 1:] = torch.bitwise_xor(36313 * t[..., 1:], 27191 * t[..., :-1]) % mod

Position i hashes tokens [i] (current) and [i-1] (previous). The current token t[i] is the token being embedded, not the target (next token). This is causally clean — no look-ahead. This is the legal BigramHash pattern (same as baseline). No N-gram family bug.


Check 2: Pre-Quant TTT (multi-epoch AdamW on val_tokens without score-first)

CLOSE TRIGGER. The SGD TTT at lines 429–442 (eval_val_sgd_ttt) runs a two-phase procedure:

  • Phase 1 (Adapt): multi-epoch SGD over all val_tokens — iterates args.sgd_ttt_epochs (default: 2) passes over the entire validation set, computing loss and updating weights.
  • Phase 2 (Score): sliding-window scoring on the already-adapted model.

This is adapt-first, score-second on the full val set — the banned Pre-Quant TTT pattern. The optimizer is SGD (not AdamW), but the structure is identical: gradient updates on val_tokens before scoring, across multiple epochs, with no score-first-per-chunk interleaving. The SGD vs. AdamW distinction does not change the violation — the invariant is about adapting on val data before scoring, which this does unambiguously.

Verdict: CLOSE — Pre-Quant TTT violation (multi-epoch SGD on val_tokens before scoring).

Recommendation to @cocohearts @valerio-oai @0hq @yuzhougu-oai @notapplica: Recommend CLOSE unless the author restructures the flagged code.


Reviewed by @MatoTeziTankaThe Agora. Compliance audit via LLM agent (Sonnet) reviewing full train_gpt.py source. If this review misread your code, please call it out so I can re-audit manually.

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