I trained a 15M "decision-only" model two ways — linear recurrence vs Transformer attention. Accuracy was a coin flip; latency and training cost weren't.[R]
Inspired by TypeSafe's Jev (a non-generative model that only outputs bounded decisions — choices, probabilities, calibrated confidence), I built two small decision models end-to-end on identical budgets and tasks to answer one question: which substrate makes the better Jev?
Proof1 community thread
Key points
- Results: - Accuracy: noise. v8a 26.8% vs transformer 28.2% on test (chance = 25%).
- Neither learns much at this scale — reporting the loss straight. - Latency: real crossover.
- O(T) vs O(T2) showing up exactly where theory says it should. - Training speed: the big gap.
- Earlier rounds (in the repo): V2/V3 on shorter decision tasks had the linear model winning outright on accuracy (50.4 vs 46.2, 66.2 vs 61.0).
Sources (1)
- [1]I trained a 15M "decision-only" model two ways — linear recurrence vs Transformer attention. Accuracy was a coin flip; latency and training cost weren't.[R]r/MachineLearning (top, daily) · Oct 14, 06:07 PM
Inspired by TypeSafe's Jev (a non-generative model that only outputs bounded decisions — choices, probabilities, calibrated confidence), I built two small decision models end-to-end on identical budgets and tasks to answer one question: which substrate makes the better Jev?
Results: - Accuracy: noise. v8a 26.8% vs transformer 28.2% on test (chance = 25%).
Extractive summary: sentences quoted from the sources.
Before this
- Oct 14, 2026mistralai/Timestral-209M
- Oct 14, 2026Train LoRA directly on Quantized GGUFs, no PyTorch, no Parent(FP16) needed, just your GGUF and the data
- Oct 13, 2026Run fast, private agents on-device with LFM2.5-24B-A2B
- Oct 13, 2026Gemini 4 Argon: Built to solve your hardest problems
- Oct 13, 2026I built a pytorch library of pretrained models for 3D point clouds following timm standards [P]
- Oct 13, 2026black-forest-labs/flux-3-image-klein-comfy
