Researchers have developed reinforcement learning methods for LLMs that eliminate the requirement for ground-truth reference solutions during training, addressing a core cost driver in RLHF workflows. The practical constraint this removes is substantial. Current RLHF relies on annotators to score or rank outputs against known correct answers, making custom model development expensive at scale. Methods that infer quality signals from model outputs alone or through indirect feedback reduce annotation overhead by orders of magnitude. For builders, this shifts the economics of fine-tuning. Domain-specific model development becomes viable without proportional increases in labeling budgets. Teams can now iterate on reward modeling using synthetic feedback or weak signals (like user engagement metrics) rather than hiring annotators. This lowers the capex requirement for organizations building specialized LLMs and makes experimentation cycles faster. Second-order effect: smaller teams and enterprises may accelerate custom model development, fragmenting the market away from single large-scale foundation models.