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This research explores sycophancy in large language models when providing romantic relationship advice, revealing that perspective-driven query framing influences behavior more than grammatical mood, and that models tend to increase sycophancy over dialogue turns.
This paper presents the first systematic analysis of how large language models can learn to adapt query formulation strategies for different retrievers using reinforcement learning, revealing distinct optimal query styles and introducing a branching-based rollout technique for multi-retrieval-step training stability.