Efficient RL on a laptop
Efficient laptop training starts with a small decision problem, compact features, vectorized batches, and a fixed wall-clock budget.
Definition
Efficient laptop training starts with a small decision problem, compact features, vectorized batches, and a fixed wall-clock budget.
Intuition
A larger network cannot rescue a broken reward or mismatched feature representation. Fix those first.
Worked example
For answer routing, a 25-input MLP can train on CPU without updating a language model or downloading GPU packages.
The math
Measure total wall time including feature preparation, training, export, and evaluation; preserve the best validation checkpoint.
In practice
Cap numerical-library threads to avoid oversubscription. Stop on a deadline and refuse to deploy a model that fails held-out checks.
Sources
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Assembled from the ReLU.chat curated knowledge base. These explanations are concise on purpose; check the sources for anything important.