Large Action Models: From Inception to Implementation

This evolution requires the transition from traditional Large Language Models (LLMs), which excel at generating textual responses, to Large Action Models (LAMs), designed for action generation and execution within dynamic environments. Enabled by agent systems, LAMs hold the potential to transform AI from passive language understanding to active task completion, marking a significant milestone in the progression toward artificial general intelligence. In this paper, we present a comprehensive framework for developing LAMs, offering a systematic approach to their creation, from inception to deployment.We begin with an overview of LAMs, highlighting their unique characteristics and delineating their differences from LLMs.

Completing a task in the real world involves a sequence of complex steps: accurately understanding user intent, devising a plan, and executing the necessary actions [28]. Current LLMs may excel at understanding and planning in textual form but often fall short when required to produce actionable outputs. This is particularly true in scenarios that demand precise task decomposition, long-term planning [12, 88], and the coordination of multi-step actions [63]. Furthermore, LLMs are generally optimized for broad, general-purpose tasks rather than tailored for specific scenarios or environments. This lack of specialization can result in suboptimal performance, especially when interacting with unfamiliar or dynamic environments where adaptive and robust action sequences are essential [39].

The process of transforming an LLM into a functional LAM involves multiple intricate stages, each requiring substantial effort and expertise. First, it is essential to collect comprehensive datasets that capture user requests, environmental states, and corresponding actions [11]. These data serve as the basis for training or fine-tuning LLMs to perform actions rather than merely generate text. This stage involves the integration of advanced training techniques that enable the model to understand and execute actions within specific environments [21]. Once the LAM has been trained, it must be incorporated into an agent system that can effectively interact with its environment. This system typically includes components for gathering observations, utilizing tools, maintaining memory, and implementing feedback loops. These components are critical for ensuring that the LAM can not only execute actions but also adapt its behavior based on real-time feedback and evolving situations [83]. The integration of these elements enhances the LAM’s capacity to perform tasks autonomously, interact meaningfully with its surroundings, and make decisions that are grounded in the context of its environment. A final but crucial step in the development of LAMs is evaluation [73]. Before deploying a LAM for real-world applications, it is imperative to rigorously assess its reliability, robustness, and safety.

2.3.1 Interpretation of User Intentions. A fundamental capability of LAMs is the ability to accurately interpret user intentions from diverse forms of input. These inputs may include natural language requests, voice commands, images, or videos, such as device screenshots or instructional videos [8]. User inputs are often abstract or implicit [6], requiring LAMs to leverage their internal knowledge and complementary information to discern the true intent behind the input. This process involves understanding nuances, disambiguating instructions, and inferring unstated objectives. LAMs must translate these user intentions into actionable plans and steps, facilitating subsequent interactions with the environment to fulfill the user’s objectives.

LAMs translate user intentions into actionable steps that can be executed within specific contexts. These actions can take various forms: operations on graphical user interface (GUI) elements, API calls for software applications, physical manipulations performed by robots, invoking other AI agents or models, or autonomously generating code or combining meta-actions [5]. By incorporating detailed knowledge of the environment, including available actions, system states, and expected inputs, LAMs can select appropriate actions and apply them correctly to meet user requests. This involves not only executing predefined actions but also adapting to new situations by generating novel action sequences when necessary.

2.3.3 Dynamic Planning and Adaptation. LAMs exhibit a sophisticated capability for dynamic planning and adaptation, which is crucial for handling complex user requests that span multiple steps [19]. They can decompose a complex task into several subtasks, each further broken down into specific action steps. This hierarchical planning enables LAMs to approach task execution with a forwardlooking perspective, anticipating future requirements and potential obstacles. Moreover, as the execution of each action alters the state of the environment, LAM will react to these changes, adapting and revising their plans and actions accordingly [58].