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This paper introduces Isolated Bilateral Reinforcement Learning (IB-RL), a method where two dialogue roles co-evolve through joint rollouts while optimizing their own rewards independently. It addresses the static-counterpart mismatch in RL for strategic dialogue, showing improved generalization to unseen counterparts in Vehicle TeleSales and Deal-or-No-Deal benchmarks.