Researchers introduce checkpoint handoff, an evaluation protocol that separates the reachability and solvability of reinforcement learning (RL) agents in live environments. This allows for a more accurate assessment of agent performance by splitting the evaluation into two components: REACH, measuring how often the agent arrives at a state close to success, and SOLVE, measuring how often it completes the task from that state. The study shows that RL agents have an advantage over stateful function transfer (SFT) agents in both terms, with a positive interaction between reacher and solver roles in all conditions.