Beyond Compliance: resistance-informed motivation alignment for Challenging Psychological Client Simulation

Large language models (LLMs) are increasingly used to simulate clients for scalable counselor training and evaluation. However, existing simulators often inherit the compliance bias of instruction-tuned LLMs, producing clients who are unrealistically open, receptive, and emotionally stable. We introduce ResistClient, a resistance-informed client simulator that models both how client resistance is expressed and why it emerges. Drawing on Client Resistance Theory, we construct the Resistance-informed Psychological Conversation (RPC) dataset by augmenting real counseling dialogues with diverse, context-dependent resistance patterns. Fine-tuning on RPC enables ResistClient to generate challenging behaviors beyond generic compliance. To ground these behaviors in coherent psychological processes, ResistClient represents client profiles using the clinical 5P case formulation, explicitly infers the client’s current motivation from the profile and dialogue context, and generates responses conditioned on this motivation. Expert-informed process rewards further align motivational reasoning and response generation with human preferences while promoting decision–response consistency. Extensive automatic and expert evaluations show that ResistClient substantially outperforms existing simulators in challenge fidelity, behavioral plausibility, and reasoning coherence. Moreover, evaluations with ResistClient reveal substantial limitations in existing psychological LLMs, including frequent client resistance, therapeutic drift, and limited progress. These findings demonstrate that resistance-aware simulation provides a more realistic and demanding environment for evaluating and improving psychological LLMs.