AgentJet让大模型智能体训练更高效稳定，支持多模型协同与故障容错。
AgentJet: A Distributed Swarm Training Framework for Agentic Reinforcement Learning
- 分布式架构分离服务端与客户端，支持灵活部署和热插拔更新。
- 在AppWorld上将执行器更新时间缩短6.25倍，显著提升训练效率。
- 适合需要长时间、多任务自动化研究的AI团队使用。
训练大语言模型智能体的强化学习策略需优化与外部环境交互的多轮轨迹。现有框架存在运行时崩溃、单模型限制、任务环境不兼容及冗余上下文等问题。我们提出AgentJet，一种基于解耦多节点架构的分布式群体训练框架。该框架将服务器-客户端拓扑设为可配置：群集服务器托管可训练模型并在GPU集群上执行优化，而可拆卸的群集客户端执行任意智能体，并通过OpenAI兼容API通信。重新配置此拓扑可支持异构多模型强化学习、隔离运行时的混合任务训练、容错执行以及通过热插拔客户端实现的实时代码迭代。AgentJet引入基于时间线合并的上下文追踪机制，在AppWorld上使演员-更新时间减少6.25倍。相同的可拆卸客户端设计还支持自动化研究系统，在大规模集群上以极少人工干预完成长达数日的长周期多任务强化学习研究。AgentJet开源且兼容发出标准LLM推理请求的智能体系统。
原文摘要 · Abstract (English)
Training reinforcement learning (RL) policies for large language model (LLM) agents requires optimizing multi-turn trajectories that interact with external environments. Existing training frameworks struggle with runtime failures, single-model constraints, incompatible task environments, and redundant context. We present AgentJet, a distributed swarm training framework based on a decoupled multi-node architecture. AgentJet treats the server--client topology as configurable: swarm servers host trainable models and perform optimization on GPU clusters, while detachable swarm clients execute arbitrary agents and communicate through OpenAI-compatible APIs. Reconfiguring this topology supports heterogeneous multi-model RL, mixed-task training with isolated runtimes, fault-tolerant execution, and live code iteration through hot-swappable clients. AgentJet also introduces context tracking with timeline merging, reducing actor-update time by 6.25x on AppWorld. The same detachable-client design supports an automated research system that conducts long-horizon, multi-day RL studies on large-scale clusters with limited human intervention. AgentJet is open-source and compatible with agent systems that issue standard LLM inference requests.
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