LLM post-training alignment - CI integrated tuning and evaluation

Executive Summary

LLMs need multi-stage post-training work to avoid hallucinations and silence. Offer a CI-integrated platform that automates supervised fine-tuning, reward-modeling, RLHF, and continuous evaluation on every commit. The source explicitly documents the three post-training phases and highlights tools like git-lrc running on every commit, which shows both the pain and a workflow to plug into. Adoption of LLMs in dev workflows is accelerating via Copilot and repo-integrated assistants, increasing frequency of model-triggered commits and the need for continuous alignment. MLOps maturity, cheaper preemptible GPUs, and hosted model infra from Hugging Face and cloud vendors make automated post-training and frequent fine-tuning operationally and economically feasible now. Upstream validation scored 88/100, indicating recurring SaaS pain exists in practice. Position as a CI-integrated post-training orchestration layer that automates the three phases described in the source - supervised fine-tuning, reward model training, and RLHF - and attaches evaluation to every commit. The source calls out git-lrc running on every commit and the article frames post-training as a recurring operational workflow, which makes a continuous, repo-integrated orchestration product a natural fit. The wedge is developer workflow integration - run reward-data collection, SFT, RL steps, and canary evaluation automatically on PRs and commits so model changes become part of standard CI.