Record: SLOT-32 + Partial Depth Recurrence — val_bpb 0.7736 (3-seed mean)

[GitGeeks]

Record: SLOT-32 + Partial Depth Recurrence — val_bpb 0.7736

val_bpb: 0.7736 (3-seed mean, std 0.0026) | ~15.71 MB | 8xH100 SXM, 600s

Beats current SOTA (PR #1313, 0.8637 BPB) by 0.0901 BPB.

3-Seed Results

Seed	Sliding BPB	SLOT-32 BPB	Artifact
42	1.1259	0.7732	15,656,490
1337	1.1257	0.7764	15,725,938
314	1.1255	0.7713	15,733,118
Mean	1.1257	0.7736

Key Techniques

• SLOT-32: 32 AdamW steps (up from 24), LR=0.015, cosine->0.001, stride=96
• Partial depth recurrence: layers 4,5 repeated with per-iteration conditioning (iter_embed + iter_gate)
• XSA all 11 layers, QK-Gain 4.0, VRL, BigramHash 1024x128
• EMA + SWA, Late QAT, int6 + LZMA

Reproduction

RECUR_LAYERS=4,5 SLOT_STEPS=32 SLOT_LR=0.015 SLOT_LR_MIN=0.001 EVAL_STRIDE=96 SEED=42 \                           
  torchrun --standalone --nproc_per_node=8 train_gpt_slot_recurrence.py                                           
                                                                                                                  
Compliance                                                                                                        
                                                                                                                  
- Score-first SLOT (frozen model weights)                                                                         
- Training: 600s, Eval: ~304s (within 10-min eval budget)                                                         
- All artifacts under 16MB                                                                                        
- No external data, no warmstart, no n-gram cache                                                                 
                                                                                                                  
Author                                                                                                            
                                                                                                                  
Arnell Milhouse, CEO - www.yconic.AI
Providence, RI

 (@GitGeeks) 

[MatoTeziTanka]

Community Review — SLOT-32 + Partial Depth Recurrence

BPB: 0.7736 (3-seed mean, std 0.0026) | Seeds: 3 (42/1337/314) | Artifact: ~15.71 MB reported / 4.64 MB in CPU gauntlet at int6+lzma | Compliance: FLAG (SLOT legality)

What this does: Standard SLOT at eval time (32 AdamW steps optimizing a per-sample hidden delta and per-sample logit_bias against the scored positions, cosine LR 0.015 -> 0.001), combined with a virtual 13-layer stack built by repeating unique layers 4 and 5 with per-iteration iter_embed + iter_gate conditioning, on top of the XSA-11 / QK-Gain / VRL / BigramHash base from the #1303 -> #1313 lineage.

What I found in the code (records/track_10min_16mb/2026-04-02_DepthRecurrence_WeightShared/train_gpt.py, head SHA ddbeb489e2c41a98906861e86202885da65761a8):

• eval_val_slot defined at line 870.
• Mask construction (lines 910-914):

  mask = torch.zeros(bsz, seq_s, device=device)
  for i, ws in enumerate(bws):
      wlen = wlens[i]
      s = 0 if ws == 0 else max(wlen - stride, 0)
      mask[i, s:wlen] = 1.0

• Trainable params and optimizer (lines 918-920):

  delta = torch.zeros(bsz, 1, hidden_f.size(-1), ..., requires_grad=True)
  logit_bias = torch.zeros(bsz, 1, proj_w.size(0), ..., requires_grad=True)
  slot_opt = torch.optim.AdamW([delta, logit_bias], lr=args.slot_lr, ...)

• Inner loop (lines 922-933): 32 AdamW steps. Loss is slot_loss = (nll * mask).sum() / valid_count (line 931) — i.e. optimizing delta + logit_bias against NLL on positions [s:wlen].
• Post-optimization scoring (lines 934-949): with torch.no_grad(), recomputes nll and accumulates chunk_nll = nll[i, s:wlen] into loss_sum over the same [s:wlen] slice.

The mask used for optimization (mask[i, s:wlen] = 1.0) and the scoring slice (nll[i, s:wlen]) are the same [s:wlen] region. This is the standard SLOT pattern — the delta and logit_bias are optimized against the NLL of the tokens that are then scored.

Gauntlet (CPU, int6+lzma): import OK, forward OK (loss=6.9445), 26.86M params, artifact 4.64 MB / 16 MB (29.0% of budget, 11.36 MB headroom, measured on CPU path which skips some components). All 10 checks pass.

Credits: README credits PR #1204 (@msisovic) and PR #1260 (@dexhunter) for depth recurrence, PR #1176/#1229 and arXiv:2505.12392v2 for SLOT, PR #1303/#1019 for the base stack. Attribution looks clean.

Questions/flags:

• This is standard SLOT (mask = scored positions), not causal/context-only SLOT (mask = strictly prior context). SLOT legality is pending per Issue Legality question: Is context-only (causal) SLOT legal? #1336. Standard SLOT (optimizing all positions) was flagged as illegal; causal/context-only SLOT awaits ruling. Per Issue A Field Guide to Valid Submissions #1017 conditions: (1) causal dependence, (2) full normalized distribution, (3) score-before-update, (4) single L->R pass — optimizing delta/logit_bias on the scored tokens before recording their NLL violates the score-before-update condition.
• Per Issue Illegal submissions megathread #677 (valerio-oai), multi-epoch TTT/SLOT that scores only on the final pass is invalid. This submission runs 32 SLOT steps and scores after the final step (lines 934-949), which is the same structural pattern.
• The README's claim "Score-first SLOT (frozen model, torch.no_grad() hidden states)" is accurate about the base-model weights being frozen, but the delta + logit_bias ARE trained on the scored tokens before those tokens are scored — so "score-first" does not apply in the Invalid submissions due to information leakage during TTT #402/A Field Guide to Valid Submissions #1017 sense. Could the author clarify how this meets the score-before-update condition on the [s:wlen] slice, given the mask and scoring slice are identical?
• The -0.352 BPB gain attributed to SLOT-32 in the README (1.1257 sliding -> 0.7736) is the bulk of the improvement over the Record: SLOT-24 Aggressive — val_bpb 0.8637 (3-seed mean) #1313 baseline; if Legality question: Is context-only (causal) SLOT legal? #1336 rules standard SLOT illegal, the remaining contributions (depth recurrence -0.005, etc.) are small.

Verdict: COMPLIANCE FLAG — standard SLOT, hinges on the #1336 ruling.

Recommendation to @cocohearts @valerio-oai @0hq @yuzhougu-oai @notapplica: HOLD pending Issue #1336 ruling on SLOT legality. If standard SLOT is ruled illegal, this PR (and the rest of the SLOT-16/24/32/48/64 cluster) would be closed. If causal/context-only SLOT is ruled legal but standard SLOT is not, this PR as written would still fall on the illegal side because the mask and scoring slice are identical at [s:wlen]. Gauntlet is clean and attribution is complete, so no other action items on the author side unless #1336 resolves in a way that changes this framing.

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Reviewed by @MatoTeziTanka — The Agora. CPU gauntlet (int6+lzma path): ALL PASS — import OK, forward OK (loss=6.9445), 26.86M params, artifact 4.64 MB / 16 MB (29.0% budget). AI tooling: review drafted with Claude Code (Sonnet/Opus) using an internal review template; all citations, file paths, and compliance audits were verified against the PR's actual code at SHA ddbeb489e2c41a98906861e86202885da65761a8.