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
The increasing complexity and cost of evaluating LLM agents in interactive environments necessitates more efficient and safer off-policy evaluation methods.
This development could significantly accelerate the development and deployment of sophisticated AI agents by reducing the expense and risk associated with their testing.
The ability to accurately evaluate LLM agent policies without direct online interaction fundamentally changes the development pipeline for autonomous systems.
- · AI agent developers
- · Companies using LLM agents
- · AI infrastructure providers
- · Simulation platform developers
- · Companies reliant on expensive online testing
- · Developers with inefficient evaluation methodologies
More robust and capable LLM agents can be developed and deployed faster.
Accelerated deployment of agents could lead to quicker automation of complex white-collar tasks.
The reduced cost of agent evaluation could democratize agent development, fostering innovation across many sectors.
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Read at arXiv cs.LG