Non-record: 10L E2E TTT-Linear + FlowRefiner — val_bpb 1.1347 (README request)#1166
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- 10-layer, 512D, E2E TTT-Linear + 1-step FlowRefiner - val_bpb 1.13472408 (int6 sliding window, stride=64, seed=42) - Artifact: 15,199,107 bytes (800K headroom under 16MB cap) - BigramHash(1536), LeakyReLU(0.5)², mixed int6/int8 + lzma - Includes three-variant size-quality comparison (11L/10L/int5) - Trained on 2×A100 PCIe 40GB, 7185 steps, ~2.2 hours
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Non-Record Submission: 10L E2E TTT-Linear + FlowRefiner (E2E was a README request)
val_bpb: 1.1347 (int6 sliding window, stride=64, seed=42) | 15.20 MB artifact | 2×A100 PCIe 40GB
Summary
10-layer transformer with end-to-end TTT-Linear refinement and a 1-step FlowRefiner, compressed to fit under the 16 MB artifact cap. The lightweight FlowRefiner is inspired in part by the FLOWR paper (arXiv:2504.10564), which uses learned flow-matching vector fields with Euler-style transport updates for efficient refinement; here we adapt that idea into a tiny hidden-state refiner rather than a pocket-conditioned 3D ligand generator.
Key Results
Three-Variant Comparison (supplementary)
Prior 11L Ablations on the Same Refiner Pair
These are earlier supporting runs on the same E2E-TTT / FlowRefiner pair from
experiments_pr549/rather than fresh 10-layer ablations for the legal submission:Synergy Note
In that earlier 11-layer study, FlowRefiner alone regressed after quantization, while the combined E2E-TTT + Flow model was best. The additive expectation from the isolated deltas is 1.12505247 BPB, whereas the actual combined run reached 1.12344104, a 0.00161 BPB improvement over additive expectation. We treat this as evidence that FlowRefiner is most useful when paired with TTT, while avoiding the claim that the same four-way ablation has already been rerun for the present 10-layer legal artifact.
Architecture
Credits
Built on PR #549 (abaybektursun) and contributions from PR #65 (aquariouseworkman), PR #69 (TevBenji), PR #187 (Idan3011), PR #265 / PR #374 (unnir), PR #315 (jfprincz), PR #77 (samacqua), PR #50 (mattqlf), PR #76 (unixmadtoonslab), and the modded-nanogpt baseline. The flow-inspired framing for the hidden-state refiner was also informed by FLOWR (Cremer et al., arXiv:2504.10564).
See
README.mdfor the detailed writeup, provenance paths to the prior 11-layer ablation logs, and supplementary variant comparison.