Definition of Large-scale Asynchronous Training Large-scale asynchronous training is a machine-learning training approach in which a large amount of computational work is distributed across many workers, devices, machines, or training processes that can perform and contribute training operations without requiring every participant to remain synchronized at each step. In conventional tightly synchronized training, participating workers commonly perform computation and then wait at coordination points so that their results can be aggregated before the next shared training step proceeds. In an asynchronous approach, different workers may instead generate training data, compute updates, evaluate model behavior, process experience, or perform other parts of the training workflow at different rates, while their outputs are collected and incorporated according to the design of the training system. This can be particularly valuable for large and heterogeneous training workloads—including reinforcement learning, agentic training, distributed model optimization, and workloads involving environments or tools with variable response times—because faster workers do not necessarily have to remain idle while waiting for slower ones. At large scale, asynchronous training can therefore improve computational utilization, throughput, scalability, and tolerance of variable workloads, although it also introduces challenges such as stale training information, uneven worker progress, coordination complexity, inconsistent model versions, and the need to determine how asynchronously generated data or updates should influence the continuously evolving model. ...

Definition of LLM Vulnerability Discovery LLM vulnerability discovery is the systematic process of identifying, examining, and validating weaknesses, failure conditions, exploitable behaviors, or insufficient protections in a large language model (LLM) $M$ that may reduce the reliability, safety, robustness, or effectiveness of the model when it is used as the reasoning and decision-making foundation of an AI agent $A$. The process may examine how the LLM responds to different inputs $x$, operates under different execution conditions $c$, and produces model outputs $y=M(x\mid c)$ in order to identify circumstances under which its behavior becomes incorrect, inconsistent, unreliable, insufficiently protected, or otherwise undesirable. Such vulnerabilities may become particularly important in agentic applications because individual model weaknesses can affect planning, reasoning, coding, tool use, information processing, evaluation, and subsequent decisions across multiple execution steps. Systematically discovering and understanding these vulnerabilities can therefore provide evidence for improving the model, its surrounding safeguards, and the design of the agentic system, thereby increasing the efficiency and reliability of AI agents when performing both simple and long-horizon tasks $\tau$, including coding, professional work, and learning tasks. ...

Definition of Long-Horizon Tasks A long-horizon task $\tau_H$ for agentic AI $A$ is a task whose successful completion requires an AI agent to sustain goal-directed reasoning and action across an extended execution trajectory $H_{\tau}={a_1,a_2,\ldots,a_T}$, where each action $a_t$ represents an operation performed at execution step $t$, and where intermediate actions, results, and decisions can influence subsequent parts of the task. Rather than being completed through a single model response or a small number of largely independent operations, $\tau_H$ typically involves multiple interdependent subtasks $S_{\tau}={s_1,s_2,\ldots,s_n}$ that may require planning, information gathering, reasoning, tool use, execution, intermediate evaluation, revision, and integration of partial results. During execution, the agent maintains a task state $x_t$ representing relevant information about progress at step $t$, including completed work, intermediate outputs, available information, unresolved requirements, environmental conditions, and other context needed to determine subsequent actions. The execution trajectory may be sequential, parallel, conditional, iterative, or recursive and may change as new information, intermediate results, errors, or environmental conditions emerge. Examples include developing and testing a substantial software system, conducting a multi-stage research project, analyzing and integrating information from many sources, or completing a professional workflow involving multiple tools and intermediate deliverables. The defining characteristic of $\tau_H$ is therefore the requirement for sustained and coherent agentic execution across an extended sequence of interdependent states and actions, placing continuing demands on goal maintenance, planning, contextual continuity, state management, tool use, intermediate evaluation, adaptation, and error recovery. ...

Definition of Training Environment Scaling for End-to-End Expert Work Training environment scaling for end-to-end expert work refers to systematically expanding the number, diversity, realism, complexity, and operational capacity of training environments $E$ in which a large language model $M$, intended for agentic applications $A$, learns to perform complete professional tasks that require sustained expert-level work across extended execution trajectories. Given an expert task $\tau$ and its sequence of interdependent subtasks $S_{\tau}={s_1,s_2,\ldots,s_n}$, an environment $E$ provides the task context, information sources, tools, resources, constraints, feedback mechanisms, and executable conditions through which $M$ can learn to progress from the initial objective to a completed and evaluated outcome. For long-horizon coding tasks $\tau_C$, such environments may include software repositories, codebases, development tools, documentation, dependency systems, execution sandboxes, test suites, debugging facilities, and evaluation mechanisms, allowing the model to perform activities such as understanding requirements, inspecting existing code, planning modifications, implementing changes, executing and testing software, diagnosing failures, revising solutions, and validating the final implementation. For long-horizon research tasks $\tau_R$, the environments may provide research questions, literature collections, databases, search and retrieval capabilities, analytical tools, computational resources, intermediate evidence, and evaluation mechanisms, allowing the model to progressively formulate research directions, gather and examine information, compare evidence, conduct analyses, revise interpretations, synthesize findings, and construct a final research output. Scaling $G(E)$ expands these environments across task volume, task diversity, difficulty, execution length, domain coverage, tool availability, environmental variation, and feedback opportunities so that $M$ gains experience with a broader range of realistic end-to-end trajectories rather than learning primarily from isolated responses or narrowly bounded subtasks. The overall purpose is to develop the model’s capacity to maintain objectives, manage intermediate states and dependencies, use tools appropriately, respond to changing information, recover from failures, evaluate its own progress, and integrate many connected operations into coherent expert work suitable for agentic execution. ...

Definition of Agent Member An agent member $(A_i)$ is an individual agent instance that belongs to an agent team $(\mathcal{A}_{\tau})$, an agent network $(\mathcal{N})$, or another multi-agent organizational structure and contributes to the execution of its assigned or dynamically determined work. Depending on the organizational structure and current execution requirements, an agent member may work individually or collaboratively with other agent members on a single subtask $(\tau_j)$, multiple subtasks $(\mathcal{T}_i)$, a complete task $(\tau)$, or, where its scope of responsibility is sufficiently broad, parts or the entirety of a project $(P)$. An agent member may also be assigned or dynamically assume a particular role $(r_i)$ and corresponding responsibilities $(q_i)$, such as planning, information retrieval, analysis, execution, coordination, review, evaluation, supervision, or integration. Its participation does not need to remain fixed throughout execution: the work, role, responsibilities, relationships, and actions $(a_{i,t})$ of an agent member may change according to the current task or project state, available information, intermediate results, environmental conditions, interactions with other members, and relevant contextual information. When operating collaboratively, an agent member may communicate with other members, exchange intermediate results and contextual information, request or provide feedback, review and critique other members’ work, propose or compare alternative approaches, debate decisions, coordinate subsequent actions, and jointly refine intermediate or final results. ...