Weber Optimizer Powers Autonomous ML Fork
π‘Physics-based optimizer + hardware entropy for autonomous MLβtest if it beats AdamW
β‘ 30-Second TL;DR
What Changed
Weber optimizer uses 19th-century electrodynamics bracket for per-parameter learning rate modulation based on velocity and acceleration.
Why It Matters
This fork could accelerate autonomous ML research by introducing novel optimizers and true randomness, potentially stabilizing training and improving results. Community-driven improvements make it accessible for experimentation on various hardware.
What To Do Next
Clone DeepBlueDynamics/autoresearch and benchmark Weber optimizer against AdamW on your H100 setup.
Key Points
- β’Weber optimizer uses 19th-century electrodynamics bracket for per-parameter learning rate modulation based on velocity and acceleration.
- β’True hardware random seeding via RTL-SDR radio receiver capturing ADC noise.
- β’Multi-provider agent.py harness supports Claude, GPT-4o, Gemini with 10 tools and thermodynamic memory.
- β’Multi-GPU support for H100 and consumer GPUs, Docker container included.
- β’Baseline improvement to 0.9697 val/bpb from community experiments.
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Original source: Reddit r/MachineLearning β
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