Experience: RLHF Code Evaluation and Logic Labeling "In a recent project focused on Reinforcement Learning from Human F
Experience: RLHF Code Evaluation and Logic Labeling "In a recent project focused on Reinforcement Learning from Human Feedback (RLHF), I was responsible for evaluating and labeling the code generation capabilities of a large language model. My day-to-day involved designing complex, edge-case prompts to test the model's proficiency in algorithm design and data structures. Once the model generated its solutions, I conducted rigorous side-by-side comparative analyses. I didn't just label the code for basic functional accuracy; I actively annotated the outputs based on strict software engineering principles, evaluating parameters like time and space complexity, modularity, and fail-fast mechanisms. By systematically grading these responses—penalizing hardcoded vulnerabilities or brute-force logic while rewarding highly scalable code—I provided the critical human-in-the-loop data necessary to refine the model's reasoning and improve its real-world coding reliability."