FitSim: Physiology-Grounded Persona Simulation for Long-horizon Fitness Coaching

Personal fitness coaching is a complex process that requires general physiology knowledge alongside an understanding of a particular trainee's needs, limitations, and how they adapt to training. LLMs can give fitness advice but struggle to accumulate training context: most benchmarks evaluate only single user-agent conversations or rely on other LLMs to role-play the user for several turns. This setup drifts away from assigned user profiles across the long horizons coaching demands. We introduce FitSim, a physiology-grounded persona simulator for long-horizon fitness coaching. Unlike other environments, FitSim does not rely on agent-to-agent messaging and treats coaching as a Partially Observable Markov Decision Process. In our simulation, a coaching agent reads wearable data projected from a dynamic persona state and recommends workouts over a year of day-by-day interactions. Each day's outcome is drawn probabilistically with respect to the agent's workout recommendation, the persona's adherence traits (motivation, discipline, trust), and evolving physiology (fitness-fatigue dynamics and an injury registry). To measure agents' performance in this environment, we introduce the Coaching Gain Ratio, which estimates the relative coaching effect over an uncoached user trajectory. Additionally, we assess coaching safety by counting overtraining and injury incidents in generated trajectories. We evaluate several frontier LLMs as coaching agents and find that they recover only a fraction of the coaching gain achievable by a parameter-free linear-deload schedule and trail the oracle reference by a wide margin.