Test-Time Policy Adaptation for Enhanced Multi-Turn Interactions with
  LLMs
Authors: name: Chenxing Wei, name: Hong Wang, name: Ying He, name: Fei Yu, name: Yao Shu

Large Language Models (LLMs) employ multi-turn interaction as a fundamental
paradigm for completing complex tasks. However, their performance often
degrades in extended interactions, as they are typically trained on static,
single-turn data, which hinders their ability to adapt to real-time user
feedback. To address this limitation, we first propose a new paradigm:
Test-Time Policy Adaptation for Multi-Turn Interactions (T2PAM), which utilizes
user feedback from the ongoing interaction as a reward signal to estimate a
latent optimal policy aligned with user preferences, then updates a small
subset of parameters to steer the model toward this policy, ultimately enabling
efficient in-conversation self-correction. We then introduce Optimum-Referenced
One-Step Adaptation (ROSA), a lightweight algorithm that operationalizes T2PAM.
ROSA guides the model parameters toward a theoretical optimal policy in a
single, efficient update step, avoiding costly iterative gradient-based
optimization and minimizing computational overhead. We provide a rigorous
theoretical analysis guaranteeing that the policy of ROSA converges to the
preference of user as the number of interactions increases. Extensive
experiments on challenging benchmark demonstrate that ROSA achieves significant
improvements in both task effectiveness and efficiency.