Prerequisites
- foundations
- training mechanics
Learning sequence
- Reference transformer math
- Attention memory traffic
- Precision and tiling
- Sparse versus dense MoE
- Correctness before performance
Hands-on lab
Match one optimized attention or matmul path against the readable reference and identify the real bottleneck with a measured curve.
Mastery gate
Explain the numerical contract, memory traffic, and why a microbenchmark may not improve end-to-end training.
CLI surface
- Run: posttrainllm bench
- Run: posttrainllm bench-train
- Run: posttrainllm experimental train-heads
How to read this dossier
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Source provenance
The normalized record comes from docs/learn/path-registry.json. The links below are the tracked evidence and explanatory sources preserved with the record.