SourceReddit r/MachineLearning•Stalecollected in 4h
First Open-Source BDH Hebbian Write-Back
#fast-weights#synaptic-plasticity#consolidationbdh-fast-weightsbdhdragon-hatchlinghebbian
💡Unlocks BDH's full potential: 99% recall via open-source fast-weight magic
⚡ 30-Second TL;DR
What Changed
Implements missing write-back using sparse activation codes as addresses.
Why It Matters
Paves way for episodic memory in inference without slow-weight corruption. Could enable continual learning in small models before scaling to language.
What To Do Next
Clone https://github.com/fleeb83/bdh-fast-weights and run n-back evals.
Who should care:Researchers & Academics
Key Points
- •Implements missing write-back using sparse activation codes as addresses.
- •Selective row-top10 consolidation preserves 97%+ performance vs dense degradation.
- •Verified 99% peak on n2/n4/n8 baselines across seeds and H100 hardware.
- •25M param model on synthetic associative recall; next: FineWeb-Edu.
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Original source: Reddit r/MachineLearning ↗
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