Mind Lab、継続学習をテストする「Macaron-V1」を発表

Chen Kaijie is a serial entrepreneur who left Duke University before graduating. He previously built MidReal, an artificial intelligence-powered interactive storytelling platform, and launched Macaron, a personal agent app that topped Product Hunt’s daily rankings on its first day. 36Kr spoke with him to learn more about Macaron and Mind Lab, the company behind it.

Mind Lab was founded in October 2025 and has more than 30 employees. Its founder, Andrew Chen, co-authored the FireAct paper with Shunyu Yao. The company’s team largely comes from xAI, DeepMind, DeepSeek, ByteDance Seed, MIT, Tsinghua University, and other companies and academic institutions.

Mind Lab’s direction closely aligns with the continual learning approach championed by Richard Sutton, a Turing Award winner widely regarded as the father of reinforcement learning, and Mira Murati, OpenAI’s former CTO.

At the main forum on the opening day of this year’s World Artificial Intelligence Conference, Sutton said the central path for the next generation of AI would be driven by experience, while the static, labeled-data paradigm had reached its ceiling.

DeepSeek has also brought continual learning to a wider audience. It has said continual learning is the problem the industry needs to solve after agents, and that it is a capability the next generation of models must possess.

Post-training and continual learning are becoming more important measures of model capability as the industry moves into its next phase.

Mind Lab released the Macaron-V1-Preview model in June. The business quickly gained momentum. Just two weeks after commercialization began, its annual recurring revenue reached USD 10 million.

Macaron-V1-Preview was built by attaching five LoRA (low-rank adaptation) expert modules to GLM-5.1. Each module had about one billion parameters.

Macaron-V1-Preview performed strongly across several benchmarks. It not only outperformed its GLM-5.1 base model but also surpassed models including GPT-5.4 and Claude Opus 4.6, according to benchmarks cited by the company. Mind Lab attributed the model’s improvement over the base model to the LoRA expert modules attached to it.

What drew attention was Mind Lab’s approach to post-training through MoL, or a mixture of LoRA adapters.

When the model performs different tasks, the system can dynamically switch to the expert module best suited to the task. As a user continues using the model, the accumulated data can also be distilled into a dedicated LoRA adapter that is continually updated as the model is called.

The preview version showed that the technical approach could work and provided initial market validation.

Mind Lab released and open-sourced the full version of Macaron-V1 on July 21. According to benchmarks published by the company, Macaron-V1 achieved state-of-the-art results in six of 12 tests. Its remaining scores were also relatively close to those of frontier models.

The release includes two models:

• The flagship version, Venti, is a 748 billion-parameter model post-trained on GLM-5.2. Of those parameters, 744 billion come from the frozen GLM-5.2 base model. The remaining four billion come from four LoRA adapters trained by Mind Lab, each with about a billion parameters and responsibility for one of four capabilities: chat, agents, coding, and user interface generation.

• The other model, Tall, is a lightweight version intended for local deployment. It has 50 billion parameters and was post-trained on Qwen 3.6.

Both versions natively support context windows of two million tokens.

In effect, the team enabled GLM-5.2 to exceed its previous capabilities by changing just four billion parameters.

For Mind Lab, entrepreneurship has been a process of repeatedly holding to a core direction while tearing down and rebuilding everything around it.

Beneath the external noise, a more far-reaching technical path has continued to evolve within the company: continual learning.

Mindverse, Mind Lab’s parent company, has raised USD 60 million since its founding. In early 2026, it completed a nearly USD 50 million Series A round led by Meituan’s investment arm, with participation from Oriza Hua, Shokz, Var Capital, and existing investors. Backers from earlier rounds include Ant Group, HSG, Being Capital, ZhenFund, and Gaorong Ventures.

Helping AI models improve over time

The company’s starting point can be traced to the FireAct paper that Andrew Chen wrote with Shunyu Yao in 2023.

At the time, they believed an agent’s task performance could be improved more effectively by training relevant data directly into the model than by relying on prompt engineering. That became the starting point for their bet on continual learning and post-training, although the technical direction had not yet attracted widespread attention.

“When we began working on continual learning, we did not even know it was called continual learning,” Chen said.

One problem with existing technology is that a general-purpose large language model struggles to adapt effectively to every possible use case.

Once training is complete, a model’s parameters are generally fixed. They do not change to reflect a particular use case, while retraining a model from scratch is extremely expensive.

A complex harness can help a large model perform tasks across different domain-specific use cases. The tradeoff is that it can consume a large number of tokens and operate slowly. That does not solve the underlying problem.

Viewed through the framework of Shannon information theory, the reinforcement learning paradigm reduces the number of parameters a representation model needs to learn.

This also means that when a model is sufficiently large and sparse, reinforcement learning within the same domain can allow LoRA-based adaptation to achieve results comparable to full-parameter training. A model can therefore be adapted to a specific use case without being fully retrained.

The team’s first major result was making reinforcement learning work at the trillion-parameter scale.

In December 2025, Mind Lab conducted end-to-end LoRA-RL training on Kimi K2, a trillion-parameter mixture-of-experts (MoE) model. LoRA-RL refers to reinforcement learning conducted using LoRA adapters.

Using 64 Nvidia H800 GPUs, it reportedly achieved results close to those of full-parameter training while consuming about 10% of the GPU resources required by conventional full-parameter reinforcement learning.

At the time, major technology companies and startups including ByteDance, Alibaba Group, DeepSeek, and Moonshot AI also had the ability to conduct reinforcement learning at the same scale.

Mind Lab is believed to be the only team in China to have made LoRA-RL work on a trillion-parameter model. Overseas, another team with similar capability was Thinking Machines Lab, founded by former OpenAI CTO Mira Murati.

One of the main difficulties in implementing reinforcement learning at this scale is that it places extremely high precision requirements on the underlying infrastructure. When the precision used during training differs from the precision used during inference, bias drift can occur, preventing the final result from converging.

Mind Lab’s solution was to design a hybrid parallel training engine that integrates tensor parallelism, pipeline parallelism, expert parallelism, and sequence parallelism. This allowed LoRA-based training to operate reliably on a MoE architecture.

The company also addressed the mismatch between training and inference by introducing truncated importance sampling to correct differences between the two distributions.

Delivering this result gave Mind Lab an important proof point. In Chen’s view, however, the company’s real technical moat lies in its infrastructure.

A year ago, the volume of post-training data was roughly one-tenth the volume of pretraining data.

Today, the amount of post-training data used in many models is greater than the amount used for pretraining.

Building infrastructure for post-training is also more difficult than building it for pretraining.

In January, Mind Lab launched MinT, an infrastructure platform for LoRA training and inference. It provides customers with a full-process solution for post-training large language models. Customers can access computing resources through the platform and train their own LoRA adapters.

MinT can manage more than one million LoRA models. During training, evaluation, deployment, and rollback, it transfers only extremely lightweight LoRA adapters, improving real-time loading speeds by nearly a factor of ten.

The company subsequently devoted more of its resources to model self-evolution and continual learning, while continuing to explore the possibilities of MoL.

Continual learning is gradually evolving from an area explored by a small number of researchers into an industry consensus.

In mid-July, Sutton entered the startup world himself and founded Oak Lab, a company dedicated to building an agent that can continually learn from its own experiences and evolve in real time.

Jie Tang, the founder of Z.ai, formerly Zhipu AI, also said in an internal letter that the next critical technologies the company must master include memory, continual learning, and self-evaluation.

As leading teams in China and overseas enter the field one after another, the pace of development is accelerating.

In Chen’s view, there are currently four approaches to continual learning:

• The first is conversational context, in which a model repeatedly interprets the user’s intent within the conversation window.

• The second is external memory, which uses retrieval-augmented generation as an added memory layer. As the volume of information to be remembered grows, the database expands rapidly. The model’s problem-solving ability then becomes increasingly tied to the quality of the database’s retrieval capabilities.

• The third is the use of harnesses and loop engineering. As a harness becomes more complex, it can consume more tokens and operate more slowly.

• The fourth is directly modifying the model’s parameters so it can adapt to a particular application. This turns the model into a domain-specific model at a foundational level, changing its performance.

LoRA belongs to the fourth category.

“We only enable LoRA mode after determining that a particular use case requires additional learning,” Chen told *36Kr*.

A general-purpose large language model cannot solve every problem found in a specific use case.

To achieve full adaptation, a model needs domain-specific training in each individual setting.

In some cases, different models can share 99% of their parameters. The remaining 1% determines how they differ.

Based on this understanding, Chen believes models should be placed in different sets of experiences and allowed to continue growing.

Mind Lab summarizes this concept as “experiential intelligence.”

A model can learn from its own experiences. Those experiences are converted into new capabilities, which allow it to solve more difficult problems. Those harder problems then generate richer experiences, turning intelligence into a process of continual growth.

The process must also be automated, as a model needs to continually evolve and iterate on itself to achieve genuine continual learning.

The direction also aligns closely with Sutton’s experience-driven approach.

The successive launches and market validation of Macaron-V1-Preview and Macaron-V1 have shown the public what experiential intelligence could make possible.

Using MoL to expand model capability

In Chen’s view, post-training is gradually becoming an industry segment in its own right.

A group of startups has emerged to take over models after pretraining and focus specifically on post-training. Many of these companies concentrate on smaller models, conducting post-training and data processing for narrowly defined, domain-specific use cases.

Mind Lab is not pursuing the LoRA path alone.

Chen said its technology is closely aligned with that of Thinking Machines Lab, particularly in its choice to train models using LoRA.

Thinking Machines Lab independently reached the same conclusion in its paper, “LoRA Without Regret”: using LoRA for reinforcement learning on a sufficiently large MoE model does not result in a loss of performance.

Reinforcement learning, however, must also be coupled with the model architecture.

Because their architectures differ, Thinking Machines Lab and Mind Lab are able to train somewhat different models.

The GLM-5 series, for example, introduced efficient inference architectures including MTP (multi-token prediction) and DSA (dynamic sparse attention). These designs impose specific adaptation requirements on training frameworks.

Thinking Machines Lab’s technology stack was designed for the standard DeepSeek-V3 architecture, making it difficult to support GLM.

Mind Lab, by contrast, was the first to complete reinforcement-learning post-training on GLM-5.1 and GLM-5.2.

It was also the first external team in the world to complete reinforcement-learning post-training on GLM-5.1.

Notably, Mind Lab equipped both Macaron-V1-Preview and Macaron-V1 with multiple LoRA expert modules. These modules can operate independently or collaborate with one another.

The company adopted this design because it found that a single LoRA adapter could not comprehensively improve all of a model’s capabilities. Using multiple LoRA adapters on the same model, however, could substantially improve its overall performance.

Mind Lab once used 200 different datasets to train 200 separate LoRA adapters, then attached them to a single model so they could collaborate on tasks.

The company made two findings:

• First, as the number of collaborating LoRA adapters increased, the model’s task performance grew in a log-linear relationship. The more LoRA adapters that worked together, the better the results became.

• Second, a model in which all 200 LoRA adapters collaborated performed better than a model using a single LoRA adapter trained on all 200 datasets.

The collaborative model delivered an improvement of about 25%.

This showed Mind Lab that model collaboration could push beyond existing limits on intelligence.

Chen said this remains an area that requires continued exploration. Among the open questions: how many LoRA modules need to collaborate for the arrangement to be meaningful, how work should be divided among them, when collaboration is necessary, and when a single LoRA module is enough.

Competition in post-training is also accelerating in China.

Companies in the sector have different strengths, but there are still relatively few teams capable of simultaneously making LoRA-RL work at the trillion-parameter scale, building infrastructure that can manage millions of LoRA adapters, and applying continual learning in commercial settings.

From post-training to continual learning

Mind Lab’s business is currently divided into three parts:

• The first is the continued development of infrastructure systems and frontier research into post-training.

• The second is updating and iterating on Macaron, its consumer product.

• The third is serving enterprise customers.

This includes deploying less expensive training infrastructure for companies such as Microsoft Azure and Huawei Cloud, as well as providing MinT and LoRA models capable of continual learning.

The Macaron app is positioned as a personal agent for everyday life.

Users can generate customized mini applications through natural language instructions.

Drawing on long-term memory and everyday conversations, Macaron assists with daily tasks and offers features such as emotional companionship.

As a consumer-facing application, Macaron also generates user interaction data that is valuable for model training. Behavior observed in real-world use can help the team better understand how users interact with models.

Mind Lab said its enterprise customers are primarily AI-enabled hardware manufacturers.

Such hardware is a natural setting for continual learning models because the data generated by each user’s interactions is almost always different.

Personalized and differentiated data is valuable for continual learning within a specific domain.

In addition to its basic model-calling services, Mind Lab plans to offer customers a continual learning option. When a customer selects the option, data associated with strong model performance will be accumulated in a dedicated LoRA adapter. The adapter will be continually trained to produce a model that is better suited to the customer’s use case and delivers greater accuracy.

In a continual learning system, LoRA can effectively produce a newly updated version every day, with each version becoming better adapted to the user’s needs and use cases.

For Mind Lab, however, commercialization is only a way to validate the practical application of its technology at this stage. It is not something the company is rushing to pursue.

Chen said Mind Lab has already reached USD 10 million in annual recurring revenue and still has considerable room to grow.

“But we do not want to put all our computing resources toward fulfilling orders,” he said. “The priority still has to be doing the research well.”

Research remains a constant part of Mind Lab’s identity. Chen said the team currently has more than 30 people working on research, including foundational infrastructure and frontier research.

“Advancing LoRA is the role we want to play,” he said. “We want to solve the final few miles after a model has completed pretraining.”

LoRA is a technical direction with many possible variations.

These include how to implement linear attention, how to extend a model’s context window, and how to improve collaboration among LoRA modules.

All of these questions still require further exploration.

*KrASIA features translated and adapted content that was originally published by 36Kr. This *article* was written by Wang Xinyi for 36Kr.*