Autoregressive Diffusion World Models for Off-Policy Evaluation of LLM Agents

arXiv:2606.05558v1 Announce Type: new Abstract: 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

Source: arXiv cs.LG — read the full report at the original publisher.

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