Prerequisites
- foundations
- evaluation and factory
Learning sequence
- Autoregressive decoding
- KV cache and prompt reuse
- Constrained generation
- Tool dispatch and routing
- Speculative decoding
- Long-context cache policies
Hands-on lab
Calculate KV memory, compare cold versus reused-prefill latency, and trace one tool request through model output, validation, and execution policy.
Mastery gate
Separate model capability, serving optimization, validation, and agent policy in one end-to-end trace.
CLI surface
- Run: posttrainllm sample
- Run: posttrainllm serve
- Run: posttrainllm agent
- Run: posttrainllm eval-bfcl
How to read this dossier
This canonical page separates the retained repository decision from the material that informed it. A source may describe an external claim, historical measurement, local observation, or planned procedure; those evidence classes are not interchangeable. Follow the provenance links before reusing a number or method.
Status and disposition describe what PostTrainLLM retained when this record was indexed. They are not a live product promise, a newly run benchmark, or permission to restart historical work. Unknown or unmeasured fields remain unknown rather than being treated as zero.
To reuse the record, first name the exact claim you need and trace it to the linked source. Then check whether the original environment, model revision, data split, evaluator, hardware, and budget match the proposed use. If they do not, treat the record as a hypothesis or design reference and run the smallest fresh comparison that can falsify it. Preserve negative outcomes and regressions beside any improvement; a local win on one slice does not silently become a general capability claim.
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.