Intro to Fine-Tuning on Modal: SFT, RL, and more

Training custom machine learning models offers a path to greater differentiation and performance optimization compared to relying solely on general-purpose frontier APIs. As products evolve into domain-specific applications, businesses often reach bottlenecks in latency, throughput, and cost, signaling a need for custom training. Modern compute platforms and open-source libraries like Hugging Face’s TRL now enable developers to train specialized models without maintaining large research teams. The GRPO (Group Relative Policy Optimization) algorithm exemplifies this shift, allowing for efficient reinforcement learning by using auto-scaling compute functions and sandbox environments to evaluate model outputs. While overfitting remains a risk, collecting rich interaction traces and designing effective reward functions allow companies to build highly capable, specialized agents that outperform generic models on specific tasks.