Conversational Recommender Systems (CRSs) provide personalized recommendations through multi-turn interactions. With the strong reasoning abilities of Large Language Models (LLMs), applying them to CRSs has become promising. Yet, existing methods often lack explicit optimization of interaction strategies, relying instead on unified prompts, which can yield suboptimal outcomes. We propose Reinforced Strategy Optimization (RSO), a hierarchical framework that decomposes response generation into macro-level strategy planning and micro-level adaptation within a network-of-experts. A Planner selects strategies (e.g., recommend, explain, encourage), while an Actor generates responses guided by auxiliary experts for preferences and factual grounding. This disentanglement enables more tractable learning. To address limited multi-turn data, we model strategy learning as reinforcement learning with an LLM-based reward for exploration. Experiments show RSO outperforms state-of-the-art baselines, validating the effectiveness of hierarchical strategy optimization.

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