Large language models (LLMs) now drive the most advanced conversational agents, creative tools, and decision-support systems. However, their raw output often contains inaccuracies, policy misalignments, or unhelpful phrasing—issues that undermine trust and limit real-world utility. Reinforcement Fine‑Tuning (RFT) has emerged as the preferred method to align these models efficiently, using automated reward signals to replace costly manual labeling.
This is a companion discussion topic for the original entry at https://aws.amazon.com/blogs/machine-learning/reinforcement-fine-tuning-with-llm-as-a-judge/