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NewsMaterial4 min readDisconfirmAI Editorial

Mistral Science VP Pierre Stock on ML4 Training: Reinforcement Learning, Ten Megawatts of Power, and European Sovereignty

In a podcast interview, Mistral Science Vice President Pierre Stock explains the training methodology for ML4: achieving efficient training through difficulty calibration in reinforcement learning, completing a trillion-parameter model using only ten megawatts of power, and arguing that open weights are a technical prerequisite for European sovereignty rather than a commercial strategy.

2026-11-06 22:00 GMT+8

Pierre Stock, Vice President of Science at Mistral, provided a detailed explanation in the heise KI-Update podcast on how this French AI laboratory trains frontier models under extreme compute constraints, and why he considers open weights to be an issue of sovereignty for Europe rather than a business model.

Stock identifies the core of ML4's training as the art of difficulty calibration within reinforcement learning. He uses an exam analogy: the model solves problems independently and receives rewards for complete or partial solutions. The entire art lies in setting the correct difficulty level—too simple, and no learning occurs; too difficult, and there is no signal. This RL approach enabled Mistral to achieve significant progress between the preview version released on October 6, 2026, and the open-weight version on October 29: the final version consumed 50% fewer tokens for equivalent intelligence, while the agent task resolution rate jumped from approximately 6% in the previous generation to nearly 60%.

This training is supported by data centers built by Mistral in France, though Stock admits their scale: ML4 was trained using only ten megawatts of power, which he describes as laughably small compared to competitors. On October 29, 2026, Mistral released the weights for ML4—a mixture-of-experts model with one trillion parameters and 49 billion active parameters per token, internally codenamed Le Chonk. Stock notes that ML4 is three times smaller than Moonshot AI's Kimi K3; quantized in FP4, it can run on four GB300 GPUs, making self-deployment possible for universities and research laboratories.

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Regarding performance, Stock claims ML4 is currently the best open-weight model for coding and cybersecurity. Benchmarks published by Mistral partially support this claim: ML4 scored 48% in the Artificial Analysis Coding Agent Index, essentially tying with Zhipu's GLM-5.3; it scored 79% in the CyberGym end-to-end benchmark, comparable to Xiaomi's MiMo-V2.6-Pro. However, Stock acknowledges that ML4 still lags behind the strongest models in coding: other companies currently possess one hundred times more resources than we do. He simultaneously claims that ML4 outperforms Anthropic's Opus 4.8, released in late May, believing Mistral is only months behind its competitors.

Stock positions open weights as the technical foundation for European sovereignty. His argument equates access to intelligence (i.e., tokens) with access to electricity: without local power production, the Industrial Revolution would not have succeeded. He states bluntly that if we abandon frontier models, we are essentially saying goodbye to the idea of Europe having a say as a superpower in the future. This stance creates tension with recommendations from European Commission President Ursula von der Leyen, who tends to favor focusing Europe on AI applications rather than developing frontier models herself. Mistral CEO Arthur Mensch had previously labeled such a route as capitulation.

On the commercial front, Mistral raised three billion euros in its latest funding round, valuing the company at over 21 billion euros, and has opened its platform for third-party models (such as China's GLM-5.3). Stock emphasizes that the company remains a frontier lab, with model development as the core; the platform strategy merely expands coverage of token demand to feed back into data center investments. For enterprise customers, the value of open weights lies in the fact that no one can cut off your access—Mistral deploys models on customers' own GPUs, after which it neither sees the model nor the data. Mozilla has already made Mistral Small 4 available as one of the optional models in Firefox's Smart Window (currently in beta).

Stock also acknowledges limitations regarding safety. In benchmarks, ML4 refuses malicious operations more frequently than Chinese competitor models, but he states this is not magic; protection mechanisms can be bypassed, and the cost of bypassing should be comparable to the cost of training a model from scratch. He advises against over-relying on benchmarks—every benchmark can be gamed—and suggests letting models of equal capability monitor each other.

It should be noted that all the above performance data, power scales, and efficiency improvements are self-reported by Mistral and have not been independently reproduced. The claim that a trillion-parameter model was trained using only ten megawatts of power would be highly significant if true, but currently lacks third-party verification. The sovereignty argument directly links base model capabilities to geopolitical influence, an equation itself worth scrutinizing—whether application-layer advantages might offer greater practical economic leverage than model-layer autonomy is a path not fully discussed by Stock in the podcast.

Sources

  • heise.deheise online↗
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