Mar 4, 2024 · Large Language Models (LLMs) have become integral components in various autonomous agent systems. In this study, we present an exploration-based trajectory optimization approach, referred to as ETO. This learning method is designed to enhance the performance of open LLM agents.

We introduce ETO (Exploration-based Trajectory Optimization), an agent learning framework inspired by "trial and error" process of human learning. ETO allows an LLM agent to iteratively collect failure trajectories and updates its policy by learning from …

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Apr 10, 2025 · Our experiments on three complex tasks demonstrate that ETO consistently surpasses baseline performance by a large margin. Furthermore, an examination of task-solving efficiency and potential in scenarios lacking expert trajectory underscores the …

Mar 27, 2025 · First, we introduce analogical reasoning and link its subprocesses to three key issues in ETO. Then, we develop theories for analogy-based knowledge transfer, rooted in the principles that underlie the subprocesses.

Oct 30, 2024 · We propose an efficient transformer-based network architecture for local feature matching. This technique is built on constructing multiple homography hypotheses to approximate the continuous correspondence in the real world and …

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Jun 11, 2024 · We present in situ, fast (1 Hz), sensitive EtO measurements made during February 2023 across the southeastern Louisiana industrial corridor. We aggregated mobile data at 500 m spatial resolution and reported average mixing ratios for 75 km of the corridor.

This paper provides an overview of existing works of ETO based on the type of problems being solved by these methods, which are ETO for Optimization in Uncertain Environment, ETO for Multitask Optimization, ETO for Complex Optimization, ETO for Multi/Many-Objective Optimization, and ETO for Machine Learning Applications.

Mar 4, 2024 · Large Language Models (LLMs) have become integral components in various autonomous agent systems. In this study, we present an exploration-based trajectory optimization approach, referred to as ETO. This learning method is designed to enhance the performance of open LLM agents.