On June 5, ByteDance's technical team revealed a blockbuster news through its official public account: by their ByteBrain team Leading, in conjunction with the University of California, Merced (UC Merced) and the University of Berkeley (UC Berkeley), together with the development of a new VMR system (Video Moment Retrieval, Video Footage Retrieval).

To put it simply, it is to enable AI to "find the second you want" from a large number of videos more efficiently and accurately. For investors, it may be the key entry point for the next "technology monetization".

What is VMR²L?

VMR²L stands for Versatile Multi-agent Reinforcement Learning with Real-time Reasoning, translated into the vernacular, is:

"A multi-agent reinforcement learning system that can make decisions in real time and is also very smart."

To put it simply, it is an AI system used to optimize the scheduling of virtual machine resources, that is, to help cloud computing and data centers quickly and efficiently allocate server resources, so that the entire system can run faster and more stable.

What's so great about it?

In the past, this type of resource scheduling problem was usually solved by a type called MIP (Mixed Integer Programming) mathematical methods to solve. Although this method is accurate, it has a fatal drawback: it is too slow! For example, in a scenario with 50 migration constraints, the traditional MIP method takes a full 50 minutes to complete the calculation. And what about VMR²L? It only takes 1.1 seconds!

Moreover, its resource utilization rate is almost not inferior to the optimal solution, and the fragmentation rate is only about 3% higher. In other words, it does it "fast and good".

Technical "God Operation": How to Do It?

At the heart of VMR²L is the perfect balance between speed and precision.

How does it do it? Here are a few key moves:

Hierarchical attention network: It's like equipping AI with a "sharp eye" so that it can see at a glance which resources are important and which can be ignored. Asynchronous Policy Gradient Algorithm + Distributed Training: Let multiple AI agents train together and make collaborative decisions, doubling the efficiency.

Dynamic graph pruning technique: Cutting off unnecessary computing nodes in real time, like pruning a tree branch, removing redundant parts and keeping only the most useful information. Combined, these technologies enable the system to complete state evaluation and action selection in milliseconds.

What does this technology mean for investment?

For investors, there are several important signals for the emergence of this technology:

Reinforcement learning is not just on paper: many people feel that reinforcement learning is too theoretical and difficult to implement. But VMR²L has succeeded in breaking this bias and proving that AI can not only "think", but also "do it immediately".

A new leap in cloud computing efficiency: With the explosion of data volumes, those who master efficient resource management technologies will be able to take the lead in the cloud services market.

ByteDance's technical strength should not be underestimated: not only is short video doing well, but the company is quietly laying out the underlying core technology, and may release more energy in the enterprise-level market in the future.

|Specializing in the treatment of traditional solutions "old problems"

In the past, when we were scheduling VMs in the data center, it was like directing traffic during rush hour – it was complicated, slow, and error-prone. And this VMR²L system is the "super traffic police" that can make optimal route planning in seconds while looking at the road conditions.

Its two biggest advantages are:

Ultra-fast response: For the first time, it realizes the "second-level linkage" with the business system, that is, it only takes a few seconds from detecting load changes to making resource adjustments.

Adaptable: Regardless of the load model of the industry (such as e-commerce promotions, live video broadcasts, and enterprise offices), its performance fluctuations are controlled within 8%, which is as stable as a Swiss watch.

How strong is the measured effect?

In real data center tests, VMR²L performed very well:

20% reduction in resource fragmentation: This means that server resources are more concentrated and the utilization rate is higher.

Save more than 5% of server procurement costs every year: the money saved is not a small amount, and it is a real profit improvement for the enterprise!

The open source ecosystem is accelerating its proliferation, and the giants are "hitchhiking"

For self-driving cars or intelligent robots in factories, "fast" is not the icing on the cake, but the ability to live and die. VMR²L's lightweight design allows it to run on edge devices, such as in-vehicle computers and industrial controllers, allowing these devices to: respond more quickly to changes in the environment; reduce reliance on the cloud; Improve the stability of the overall system. This is not a small step, it is a big step towards "autonomous intelligence"!

The open source ecology is accelerating its proliferation, and the giants are "hitchhiking", and the most exciting thing is that the core code of this system has been open-sourced! Right on GitHub, you can freely download, test, and improve.

A number of tech giants are beginning to participate in the adaptation; Different industries have tried to apply it; The speed of technology diffusion has accelerated dramatically.

ByteDance is already planning a next-generation system, VMR³L. The goal this time is to add neural symbolic inference technology to make AI decision-making more "logical" and no longer a "black box operation"; Explore the potential of quantum computing in ultra-large-scale scheduling, and lay out the next generation of computing paradigms in advance. While these directions are still in the exploratory stage, they represent a clear trend: reinforcement learning is no longer just "smart", but is becoming more practical, transparent, and powerful.