Autoregressive Diffusion World Models for Off-Policy Evaluation of LLM Agents
Evaluating large language model (LLM) agents in multi-turn interactive environments is expensive and risky, as it requires online environment interaction. We propose ADWM (Autoregressive Diffusion World Model), an evaluation framework that estimates the performance of a new LLM agent policy purely from pre-collected trajectories. The core idea is to learn a latent diffusion world model that simulates how the environment responds to the evaluation policy, without ever executing it in the real environment. Existing diffusion-based OPE methods guide full trajectories in a single pass by jointly diffusing states and actions, an assumption that breaks down for LLM agents whose actions are disc...
| Authors | Kaixuan Liu, Guojun Xiong, Weinan Zhang et al. |
|---|---|
| Primary category | cs.LG |
| Published | 4 June 2026 |
| Last updated at source | 20 July 2026 |
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