Record: SP8192 + CaseOps + Gated Attention + Quant Gate + Loop4-5 + Phased TTT + Frozen Recurrent Alpha — val_bpb 1.06421#1779
Conversation
3-seed mean val_bpb = 1.06421 (std 0.00023), val_loss = 2.32888 nats/token. -0.00128 BPB vs base submission PR openai#1736 (1.06549). Adds frozen cross-layer recurrent alpha/beta scalars (RECUR_ALPHA_ENABLED=1, NUM_LOOPS=2) on the openai#1736 base. Scalars trained to convergence then frozen as constants baked into the artifact. Also includes LoRA-TTT improvements from PR openai#1767 (warm-start A, alpha=144, WD=1.0). All 3 seeds clear 16 MB decimal cap (max 15,976,882 B) and both 600s budgets.
leon2k2k2k
changed the title
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Hi @leon2k2k2k — nice work on the frozen recurrent alpha/beta mechanism, interesting pattern. Heads-up that we just patched the same bug in PR #1736 and PR #1769 and wanted to flag it here since this submission forks the same prep script. Issue. Scope. Prep-script only — the submitted 1.06421 is on valid data if your seed runs used shards produced by an internal pipeline that already prepends BOS. Fix — 4-line diff, matches the canonical pattern in # near module top, with other constants
BOS_ID = 1
# inside the per-doc loop
for text in _iter_docs(args.docs):
transformed = encode_lossless_caps_v2(text)
token_ids = [BOS_ID] + sp.encode(transformed, out_type=int)
if n_docs < args.val_docs:
byte_counts = _token_original_byte_counts(sp, text, transformed)
val_buf_tokens.extend(token_ids)
val_buf_bytes.append(0) # BOS = 0 original bytes
val_buf_bytes.extend(int(b) for b in byte_counts)
else:
train_buf.extend(token_ids)Our fix commits if useful for reference — same diff applies cleanly:
Originally reported by @codemath3000 on PR #1736. Adding a |
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Flagging the recur_beta / recur_alpha buffers legality: self.register_buffer("recur_beta",
torch.tensor([1.5973426, 1.8826205, 1.9906198], dtype=torch.float32))
self.register_buffer("recur_alpha",
torch.tensor([[ 0.251953125, -0.02099609375, -0.01239013671875],
[ 0.06689453125, -0.34765625, 0.0031280517578125],
[ 0.138671875, 0.2412109375, 0.0272216796875]],
dtype=torch.float32))These 12 floats were "trained to convergence then frozen as constants" — i.e. discovered by offline gradient descent on the training loss and baked into the shipped artifact outside the 10-minute training budget. Arguments for legal: all 48 bytes are charged against the 16MB cap, they're registered openly as buffers rather than hidden, and the README explicitly permits offline tuning ("Tuning your Adam hyperparameters across a bunch of runs is fine"). Arguments for contested: unlike hyperparameters found by search (which encode "what config generally works for this architecture"), these values are gradient-descent outputs on FineWeb training data — they carry task-specific learned signal that required task-relevant gradient steps outside the budget to discover, and the register_buffer + bake-in-converged-values pattern generalizes to arbitrarily many pre-trained weights if precedent is set at 12 floats. Perhaps violates the spirit of the competition. Just surfacing for clarification. |
3 participants
Summary
val_bpb = 1.06421 (3-seed mean, std 0.00023) | ~15.98 MB | 8×H100 SXM, 600s train / 600s eval | Phased TTT
RECUR_ALPHA_ENABLED=1 NUM_LOOPS=2), and LoRA-TTT improvements (warm-start A, alpha=144, WD=1.0) from PR Record: Alpha=144 LoRA + Warm-start A + WD 1.0 — val_bpb 1.07209 (3-seed mean) #17673-Seed Results
All 3 seeds clear both 600s budgets (train + eval) and the 16,000,000-byte decimal artifact cap.
Key Techniques
NUM_LOOPS=2, recurrence on layers 3–5.RECUR_ALPHA_ENABLED=1) trained to convergence then frozen as constants. Converged values baked into the artifact. L4 self-subtract (α=−0.348) acts as a learned gate; L5 aggregates signal from L3+L4.Rule Compliance
Test Plan
train_gpt.pyand env vars from README< 16,000,000bytes in each seed log🤖 Generated with Claude Code