Fine‑tuning small LLMs today means more than adjusting weights: it’s building reproducible pipelines for supervised reward learning (SRL), RLHF-style preference modeling, evaluation, and safe deployment. This topic covers the tool classes and commercial services that teams use to collect interaction data, label preferences, train reward models, run offline and online RLHF loops, and integrate tuned models into agents and production workflows. Why this matters in late 2025: many organizations prefer smaller, specialized models for cost, latency, privacy, and regulatory control. That shift has driven mature tool categories — AI Data Platforms for collecting and curating human feedback, RLHF/SRL toolkits for reward modeling and policy optimization, developer agent frameworks for orchestration and testing, and marketplaces for managed models and agent components — all essential to operationalize fine‑tuning at scale. Representative tools and roles: OpenPipe provides a managed pipeline to collect LLM interactions, fine‑tune models, run evaluations, and host optimized inference; LangChain supplies the engineering primitives to build, test, and deploy reliable AI agents and to orchestrate fine‑tuned models in production; LlamaIndex focuses on document agents and RAG orchestration, important for domain alignment before or after fine‑tuning; Lindy offers no‑/low‑code agent creation and governance for non‑engineering teams; Adept targets enterprise automation where agentic models interact with software interfaces; and Anthropic’s Claude family serves both as a commercial model baseline and evaluation target. Practical considerations include choosing SRL vs. RLHF depending on data volume and safety needs, instrumenting feedback collection, continuous evaluation and regression testing (GenAI test automation), and integrating tuned models into agent marketplaces and governance workflows. The ecosystem now emphasizes end‑to‑end pipelines that link data platforms, RLHF toolkits, agent frameworks, and managed commercial offerings to produce aligned, maintainable small LLMs.