Human Data is (Probably) More Expensive Than Compute for Training Frontier LLMs

Post-training techniques (e.g., supervised fine-tuning and reinforcement learning with verifiable rewards) are crucial to recent advances in LLMs. Unlike pre-training, post-training relies heavily on annotated data provided by humans, often requiring expert input. Fine-tuning with reinforcement learning, the core technique powering today’s most advanced reasoning models, demands not only high-quality data but also verifiable answers. “Scale AI expects to more than double sales to $2 billion in 2025. The startup generated revenue of about $870 million last year,” reported by Bloomberg. The incredible demand for high-quality human-annotated data is fueling soaring revenues of data labeling companies. In tandem, the cost of human labor has been consistently increasing. We estimate that obtaining high-quality human data for LLM post-training is more expensive than the marginal compute itself1 and will only become even more expensive. In other words, high-quality human data will be the bott

Post-training techniques (e.g., supervised fine-tuning and reinforcement learning with verifiable rewards) are crucial to recent advances in LLMs. Unlike pre-training, post-training relies heavily on annotated data provided by humans, often requiring expert input. Fine-tuning with reinforcement learning, the core technique powering today’s most advanced reasoning models, demands not only high-quality data but also verifiable answers. “Scale AI expects to more than double sales to $2 billion in 2025. The startup generated revenue of about $870 million last year,” reported by Bloomberg. The incr