As AI agents move from laboratories to large-scale applications, the supporting capabilities of the underlying infrastructure are facing unprecedented challenges.

Recently, MiniMax and 腾讯云 announced a deep collaboration and successfully completed an important practice in agent infrastructure. Relying on 腾讯云's powerful computing scheduling and cloud-native capabilities, MiniMax has started deploying a Agent RL (Agent Reinforcement Learning) sandbox with millions of throughput and tens of thousands of concurrent connections, and it has achieved full stable operation in the test environment.

Reinforcement learning (Reinforcement Learning) is key to enhancing the decision-making capabilities of AI agents. However, large-scale agent training often comes with high computational costs and environmental construction pressure. The core highlight of this collaboration is that 腾讯云 has helped MiniMax's reinforcement learning framework Forge achieve a qualitative leap:

Extreme efficiency: The training environment supports "second-level activation," significantly shortening the experiment preparation time.

Resource optimization: Achieving dynamic resource management with "use and then delete," ensuring that computing resources are not wasted.

Cost reduction and efficiency enhancement: Under the condition of ensuring a more stable and faster training process, it significantly reduces the overall cost of large-scale training.

As an AI newcomer with a valuation exceeding traditional internet giants, MiniMax has been active in both the capital market and the technology field recently. Not only has its market value continued to rise, but its overseas market share has also exceeded 70%. This collaboration with 腾讯云 is not only a win-win in the technical field, but also provides an industry reference "standard model" for large-scale deployment of agent sandboxes.

As the雏形 of the "operating system" of the AI era begins to emerge, a more efficient underlying sandbox will become an accelerator for agent evolution. As MiniMax continues to deepen its research in the field of reinforcement learning, a million-level agent ecosystem capable of self-learning and rapid iteration is getting closer to real life.