LLM post-training guide: methods and evaluation
Published on October 12, 2025, the guide covers adapting LLMs to follow instructions, post-training methods, and approaches to evaluating results.
Source: Um guia de post-training de LLMs (tokens-for-thoughts.notion.site). Text prepared with AI from this source.
What happened and what to do
An entry dated October 12, 2025 describes a guide about the transition from next-token prediction to instruction following. Its scope includes SFT data and objectives, RLHF, RLAIF and RLVR, reward models, and evaluation frameworks. The entry does not include a URL: to consult and verify the original, locate it by title and check its date and the topics described in this reference.
For a company adapting models, these topics can inform an engineering process: define tuning objectives and data, establish reproducible evaluations, and monitor results before making a model available. The right work depends on the case; it is not necessary to adopt every technique or use AI when a simpler solution meets the need.
How the consultancy can help
Wendelmaques can diagnose your case’s objectives, data, and evaluation risks, then propose a scoped implementation of tuning, testing, and monitoring, with support for operating it on your infrastructure.
Next step
Send a short description of the model, its objective, and the data involved to receive a proposal scoped to your case.
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Infrastructure review, deployment and ongoing operations, with scope and pricing defined in the proposal.
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