Researchers proposed MICA, a reinforcement learning framework for multi-turn emotional support dialogues. MICA addresses the challenge of sparse rewards and poor credit assignment by deriving both immediate and delayed credit from a shared potential function over the user's structured support state. MICA uses two signals: Incremental Distance Reward measures per-turn progress, while Monte Carlo return captures delayed effects. The framework normalizes these signals and combines them into a mixed advantage, which enables stable per-turn optimization without the need for matched-state comparisons or a learned critic. MICA outperforms existing methods on several emotional support benchmarks, with improvements of up to +42.5, and does not incur additional rollout costs. This advance has practical implications for building and operating AI agents in interactive applications, enabling more effective and efficient multi-turn reinforcement learning.