How would you design a reward for a general-purpose ass...

For general-purpose assistants, avoid a monolithic reward model. Instead, layer verifiable signals (e.g., format compliance, citation presence, tool-call correctness) with a preference RM for subjective quality. Use LLM-based judging for specific safety dimensions to improve diagnostic clarity. Finally, evaluate the model as a multi-objective Pareto surface to track trade-offs across dimensions rather than relying on a single aggregate score.