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language:
  - en
  - pl
  - cs
license: apache-2.0
tags:
  - bdh
  - linear-attention
  - scaling-laws
  - replication
library_name: pytorch

BDH vs Linear-Attention Baselines — Replication Scaling Study

Independent replication of the scaling experiments from "Burst Denoising Hebbian Neural Networks" (Pathway, arXiv:2509.26507) and a head-to-head comparison of BDH-GPU against GPT-XL, GLA, DeltaNet, Mamba-2 at matched params, same tokens, two GPU replicas (RTX 4080 SUPER, A100-80GB).

Bottom line: BDH reaches consistently lower validation loss at 25M/50M/100M (~0.5–0.8 nats below the best baseline, stable across both GPUs).

Results — best validation loss (4080 / A100)

model	25M	50M	100M
BDH	2.5975/2.7917	2.6090/2.7555	2.7361/2.8117
GPT-XL	3.2441/3.3866	3.2476/3.4039	3.2842/3.3387
GLA	3.4040/3.5511	3.3767/3.5443	3.4605/3.6203
DeltaNet	3.3881/3.5556	3.3726/3.5684	3.4601/3.5705
Mamba-2	3.2357/3.4403	3.2351/3.3686	3.2360/3.3898

Protocol

Data: Europarl en-pl + en-cs (~1.28M pairs), byte-level UTF-8, <F:src>SRC<T:tgt>TGT format (paper Appendix B.1).
Training: AdamW lr 1e-3→1e-4, warmup 1000, wd 0.1, seq_len 2048 (100M: 1024), 4000 steps, TBPTT.
Models matched at equal total params; baselines use published fla kernels.
Full writeup: see PAPER.md.