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MiniMax and Tencent Cloud Partner to Achieve Full Stable Operation of RL Sandbox for Million-Level Agent Training
The shift of AI agents from research labs to real-world applications is placing unprecedented demands on the infrastructure that supports them.
Recently, MiniMax and Tencent Cloud announced a deep partnership and successfully completed a key milestone in agent infrastructure. Leveraging Tencent Cloud 's powerful compute scheduling and cloud-native capabilities, MiniMax began deploying an agent reinforcement learning (RL) sandbox with throughput in the millions and tens of thousands of concurrent connections, achieving full stability in the test environment.
Reinforcement learning is essential for improving AI agents' decision-making. However, large-scale agent training often brings high computational costs and environment setup challenges. The standout achievement of this collaboration is that Tencent Cloud helped MiniMax 's RL framework Forge make a major leap forward:
Extreme efficiency: The training environment supports "second-level activation," drastically cutting experiment preparation time.
Resource optimization: Dynamic resource management with a "use-and-release" approach ensures no computing power is wasted.
Cost reduction and performance boost: A more stable, faster training process significantly lowers the overall cost of large-scale training.
As an AI startup valued higher than some legacy internet giants, MiniMax has been active on both capital and technology fronts. Its market value has continued to rise, and overseas market share now exceeds 70%. This partnership with Tencent Cloud is not just a technical win-win; it also sets an industry benchmark for large-scale agent sandbox deployment.
As the prototype of an AI-era "operating system" begins to take shape, a more efficient underlying sandbox will accelerate agent evolution. With MiniMax deepening its reinforcement learning research, a million-level agent ecosystem capable of self-learning and rapid iteration is drawing closer to reality.
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The shift of AI agents from research labs to real-world applications is placing unprecedented demands on the infrastructure that supports them.
Recently,
Reinforcement learning is essential for improving AI agents' decision-making. However, large-scale agent training often brings high computational costs and environment setup challenges. The standout achievement of this collaboration is that
Extreme efficiency: The training environment supports "second-level activation," drastically cutting experiment preparation time.
Resource optimization: Dynamic resource management with a "use-and-release" approach ensures no computing power is wasted.
Cost reduction and performance boost: A more stable, faster training process significantly lowers the overall cost of large-scale training.
As an AI startup valued higher than some legacy internet giants,
As the prototype of an AI-era "operating system" begins to take shape, a more efficient underlying sandbox will accelerate agent evolution. With
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