← 返回
金融论文研究#EquiLibre Technologies#博弈论#强化学习#扑克AI#量化对冲基金

前DeepMind团队创办的EquiLibre Technologies估值超5亿美元,其扑克AI技术正为量化对冲基金赚钱

TechCrunch:AI(RSS)T2
04:3307/01 04:33社区阅读原文 ↗导出 MD ↓

评分

51

精选理由

该事件体现了AI技术在金融领域的实际价值创新,且由顶尖团队实施,影响力较高。

AI 点评

「这标志着顶尖AI博弈技术从游戏领域成功跨行业落地金融投资,展示了强化学习在非完美信息场景下的商业潜力。」

速览

由三位前DeepMind研究员在布拉格创立的AI实验室EquiLibre Technologies,其估值已超过5亿美元。该团队曾开发出顶尖的扑克AI,如今正将这项技术应用于量化对冲基金领域,通过博弈论与强化学习为金融机构创造收益。

  • 三位前DeepMind研究员在布拉格创立EquiLibre Technologies
  • 团队曾开发出高水平扑克AI,现转向量化金融
  • 估值超5亿美元,利用博弈论与强化学习为对冲基金赚钱

正文

Three former DeepMind researchers who created an AI that beat humans at poker have now applied the same technology to trading stocks — and the bet appears to be paying off. Their Prague-based AI lab, EquiLibre Technologies, is now valued at $500 million after raising an undisclosed-sum Series A, TechCrunch learned.

The round was led by Creandum, and, although the VC also declined to disclose the size of the round, vice president Cameron Sellers confirmed that it was the largest single investment the firm “has ever made in one go into a company,” he told TechCrunch.

The common denominator between poker and Wall Street is that they are well suited for reinforcement learning, an AI training technique where self-learning models are incentivized by rewards. According to Martin Schmid, EquiLibre CEO, “The nice thing about trading and markets is that the scoring is super simple: how much money did the agent make?”

This isn’t just game money. In partnership with quant firm Tower Research Capital, EquiLibre’s algorithms have been trading billions in daily volume across the S&P 500 and Nasdaq. The startup claims its agents have been doing well since their rollout on crypto markets in 2025, and now on stock exchanges, with “a perfect record of zero negative months since inception,” meaning they have finished each month with their investments up overall.

By applying its AI to quant hedge funds, the startup is in a field where automation is commonplace and, if successful, improvements can quickly turn into cash. That made the startup appealing to Creandum, Sellers said.

“The potential total addressable market of trading in the financial markets is one of the biggest on earth, and there are countless funds over the years that have generated quantums of profit that make most venture-backed successes look small,” Sellers said. But he noted that EquiLibre explicitly defines itself as “a lab first, not a finance firm.”

Schmid and his two founders — CTO Rudolf Kadlec and CSO Matej Moravcik — don’t have a background in finance, and it is not what drives them, he told TechCrunch. “I’m not doing this because I’m excited about making markets efficient. I’m doing this because we are all excited about building new things that have never been built before, and this is a lot of fun to build,” Schmid said.

The prospect of frontier AI by by DeepMind alumni is an area of hot pursuit by VCs as well. Another recent such example is Ineffable Intelligence, which recently raised 1.1 billion. Most of these are based in the U.K., but there are notable exceptions, including EquiLibre. 

In the case of EquiLibre’s founding trio, they were visiting PhD students at the Google-owned company’s first international AI research office in Edmonton, Alberta, Canada (which Alphabet shut down in 2023.) While there, they built DeepStack, the first AI program to defeat pro players at no-limit poker, also known as Texas hold ’em. They also worked with professors who are now part of the startup’s high-profile advisory board — including Rich Sutton, who went on to receive the Turing award in 2024 for his work on reinforcement learning.

To build their startup, EquiLibre’s founders decided to move back to their home country, Czechia. “This is where we had a lot of people we had worked with, and there was a large Czech diaspora at Google and other places,” Schmid said. “These were our friends, so we told them, ‘Hey, guys, we are moving back to Prague, do you want to join us?’”

That helped EquiLibre build its initial team back in 2022 and reach its current headcount of 25 people; but according to Schmid, that choice of location keeps paying dividends. Compared to San Francisco, “It’s much easier to keep the good people here, because there’s not a new sexy AI thing happening every two months.”

Not that EquiLibre is the only hot AI startup in town. BottleCap AI is based in the same building.

Still, this is one of the more notable AI companies in the region for talent. It next plans to scale its compute infrastructure, bringing online what it expects will be one of the largest compute clusters in Central and Eastern Europe (CEE).

While the startup also declined to disclose its total funding to date, Schmid said it previously raised two other funding rounds, with pre-seed backers including CEE-focused VC firm Credo, which also backed ElevenLabs and UiPath. According to Dealroom data, EquiLibre’s $10 million seed round was led by Blossom Capital at a $140 million valuation.

Sellers confirmed that the Series A $500 million valuation was a big jump. But it also comes after the winds have changed favorably for reinforcement learning (RL), including in trading. “When we started, people were skeptical,” said Schmid. But now RL is the standard. “Because we started four years back, we believe we are ahead.”

Still, there is a risk that the startup will get leapfrogged by competitors. Trading giant Jane Street, for instance, states it already uses RL with LLMs, “or whatever else we need to train good models.” It also claims it has “tens of thousands of high-end GPUs,” while EquiLibre is seeking to squeeze more compute out of way fewer chips and “get more from less,” Schmid said.

Considering how profitable Jane Street is, EquiLibre will have to play its cards well in order to reach its goal to be known as “the AI lab in trading.” But this isn’t poker, and there might be no losers. Says Schmid: “This is not a winner-takes-all market.”

Topics

AI, Creandum, Czech Republic, equilibre technologies, Europe, Exclusive, prague, reinforcement learning, Startups, stock market, Venture

When you purchase through links in our articles, we may earn a small commission. This doesn’t affect our editorial independence.

Anna Heim Anna Heim

Freelance Reporter

Anna Heim is a writer and editorial consultant.

You can contact or verify outreach from Anna by emailing annatechcrunch [at] gmail.com.

As a freelance reporter at TechCrunch since 2021, she has covered a large range of startup-related topics including AI, fintech & insurtech, SaaS & pricing, and global venture capital trends.

As of May 2025, her reporting for TechCrunch focuses on Europe’s most interesting startup stories.

Anna has moderated panels and conducted onstage interviews at industry events of all sizes, including major tech conferences such as TechCrunch Disrupt, 4YFN, South Summit, TNW Conference, VivaTech, and many more.

A former LATAM & Media Editor at The Next Web, startup founder and Sciences Po Paris alum, she’s fluent in multiple languages, including French, English, Spanish and Brazilian Portuguese.

View Bio Event Logo November 4 Boston


Last chance to save up to $190 on TechCrunch Founder Summit. Join 1,000+ founders and VCs at all stages for real-world scaling insights and connections that move the needle.

Savings end June 26, 11:59 p.m. PT.

REGISTER NOW

Most Popular

相关条目

85

五维评分

信息增量10 ×0.3

该基准填补了现有记忆基准无法评估深层用户理解的空白,信息增量大。

内容质量9 ×0.2

理论扎实,实验设计系统,分析深入。

实用价值9 ×0.2

为个性化Agent开发提供评估工具和方向,实用价值高。

时效性10 ×0.15

当前Agent热潮下,用户理解是关键问题。

信源可信度9 ×0.15

来自arXiv论文,可验证;但未正式发表,稍减1分。

基础分 94.5 × T2 信源权重 ×0.9 = 85

精选线 70已入选精选

23:47arXiv AI/CL/LG(论文)T2论文

精选Setoka:面向异构数据个性化Agent的层级用户理解基准

这篇论文非常值得关注,尤其对于从事个性化Agent、对话系统和记忆增强AI的研究者和工程师。它不仅仅是一个基准,而是从心理学出发定义了用户理解的层级,揭示了现有系统在高层理解上的系统性失败。如果你在设计基于记忆的Agent,这篇文章会提醒你记忆系统需要支持跨时间、跨来源的抽象推理,而不仅仅是检索。

AI论文#Setoka#个性化Agent#用户理解基准
85

五维评分

信息增量10 ×0.3

论文首次提出在高维分布估计中实现多项式收敛速率的可证明方法,填补了稀疏贝叶斯网络在非参数密度估计中的理论空白。

内容质量9 ×0.2

理论推导严谨,实验设计全面覆盖合成与真实数据,与多种基线对比,但缺少大规模超高维(d>1000)场景验证。

实用价值9 ×0.2

BAND方法可直接用于金融波动率建模、气候数据密度预测等实际高维时间序列任务,且支持混合数据类型。

时效性10 ×0.15

2025年7月发布的最新论文,针对当前高维数据建模的痛点,具有实时参考价值。

信源可信度9 ×0.15

来源于arXiv(CS/LG领域一流预印本平台),论文为预印本,尚未经同行评审,但附有可复现代码且作者单位知名。

基础分 94.5 × T2 信源权重 ×0.9 = 85

精选线 70已入选精选

22:21arXiv AI/CL/LG(论文)T2论文

精选BAND:高维分布估计的贝叶斯网络回归方法——打破维度诅咒

这篇论文值得所有从事高维统计、概率建模和生成式AI的研究者关注。它从理论上给出了一个简单却有力的答案:稀疏结构能打破维度诅咒,而贝叶斯网络恰好提供了自然框架。相比VAE/GAN这类黑箱方法,BAND可解释性强且收敛有保证,尤其适合工业界对可靠置信区间的需求。如果你是研究时间序列预测或风险管理的,这篇论文可能会改变你处理高维密度估计的方式。

AI论文#BAND#稀疏性#贝叶斯网络
83

五维评分

信息增量10 ×0.3

首次实现三维经典密度泛函的等变学习,突破了以往低维限制,填补了该方向的空白。

内容质量9 ×0.2

方法严谨,验证丰富,从基础热力学量到复杂几何应用层层递进,结论扎实。

实用价值8 ×0.2

学习到的泛函可直接迁移用于新系统,有潜力替代大量昂贵原子模拟,实用性强。

时效性10 ×0.15

紧贴AI for Science前沿热点,恰逢等变模型在科学计算中快速发展的时期。

信源可信度9 ×0.15

来自arXiv预印本,但作者团队背景和论文质量表现出高水平,可信度较高。

基础分 92.5 × T2 信源权重 ×0.9 = 83

精选线 70已入选精选

01:32arXiv AI/CL/LG(论文)T2论文

精选等变学习可迁移的三维经典密度泛函

这篇论文最值得关注的点在于,它把机器学习和统计力学的核心难题直接对接,用等变网络学出了一个可迁移的密度泛函,而且不依赖任何标签。对于做液体物理或软物质的同行,这可能是新的研究范式:以后是不是不用再针对每个体系从头做模拟了?对于AI研究者,这也展示了如何将物理对称性和变分原理嵌入模型,而不是单纯拟合数据。建议重点读一下方法部分,尤其是等变性和变分一致性的实现方式,那才是真正的精髓。

AI论文#AI for Science#arXiv#图神经网络
83

五维评分

信息增量10 ×0.3

首次为迭代整流流给出严格的计算与统计保证,填补了该领域重要理论空白。

内容质量9 ×0.2

包含完整定理、反例与收敛速率分析,理论推导严谨且结构清晰。

实用价值8 ×0.2

为设计成本感知的整流流算法提供了明确条件,可直接指导图像生成模型训练。

时效性9 ×0.15

紧跟整流流在主流生成模型中的广泛应用,属于当前热点方向的前沿进展。

信源可信度10 ×0.15

来自 arXiv 预印本,且与 FLUX.1 和 Stable Diffusion 3 等真实系统直接相关,来源可信。

基础分 92.5 × T2 信源权重 ×0.9 = 83

精选线 70已入选精选

00:53arXiv AI/CL/LG(论文)T2论文

精选c-Rectified Flow 的计算与统计保证

这篇论文真正把整流流从“经验好用”推向了“理论可控”,对做生成模型底层算法的人尤其重要。如果你在调 FLUX 或 SD3 这类管线,理解普通整流流在协方差不可交换时可能失效,能帮你避开一些隐蔽的训练陷阱。

AI论文#arXiv#FLUX.1#Stable Diffusion 3
83

五维评分

信息增量9 ×0.3

论文提出完全替代真实遥操作数据的方案,并开源大规模数据集,信息增量大。

内容质量9 ×0.2

实验设计严谨,覆盖多个骨干网络和任务,结果量化清晰,方法描述完整。

实用价值10 ×0.2

数据系统便携且低成本,可大幅降低机器人学习门槛,实用价值极高。

时效性10 ×0.15

2025年最新研究,开源于arXiv,符合当前机器人学习对高保真数据的需求。

信源可信度8 ×0.15

来源arXiv预印本,来自学术机构,方法透明且代码数据集开源,可信度较高。

基础分 92 × T2 信源权重 ×0.9 = 83

精选线 70已入选精选

23:52arXiv AI/CL/LG(论文)T2论文

精选从高保真UMI数据学习可部署操作策略

这篇论文最亮眼的地方是证明了纯无机器人数据也能达到与真实遥操作相当的性能,彻底打破了后训练阶段必须依赖少量真实数据的惯例。对于想降低硬件成本的研究组和工业机器人应用开发者来说,HiFi-UMI的数据生产方案和开源数据集是直接可用的资源。建议关注其头戴式SLAM和微秒同步的工程细节,这些是精度达标的关键。

AI论文#HiFi-UMI#SLAM#UMI
83

五维评分

信息增量9 ×0.3

提出了全新的七维评估框架,填补了金融领域LLM工作流输出可靠性评估的空白。

内容质量9 ×0.2

实验设计全面,包含多个模型、多个工作流和独立评审,分析深入且有具体数值支撑。

实用价值10 ×0.2

对金融机构评估和选型LLM有直接指导作用,可降低部署风险。

时效性10 ×0.15

来自arXiv最新预印本,反映当前最前沿的研究动态。

信源可信度8 ×0.15

来自知名预印本平台arXiv,论文结构规范,但尚未经过正式同行评审。

基础分 92 × T2 信源权重 ×0.9 = 83

精选线 70已入选精选

22:10arXiv AI/CL/LG(论文)T2论文

精选资本市场LLM可靠性评分:从“看似合理”到“可银行化”

这篇论文对金融圈和AI圈都很有价值:它给出了一个可操作的、面向实际工作流的评估框架,比泛泛的基准更贴近真实需求。如果你在金融科技公司做LLM落地,或者负责模型的“可解释性”和“可审计性”,这篇值得细读。决策有用性维度的发现尤其值得关注——评审们一致认为这是关键,但不同模型表现差异最大。

AI论文#Anthropic#Llama 3.3 70B#LLM评估
83

五维评分

信息增量9 ×0.3

提出优化器状态位置与数量同等重要的新视角,对MoE内存优化提供了独特解决方案。

内容质量9 ×0.2

实验设计完整,包含消融、基线和超参数调优,结论可靠。

实用价值10 ×0.2

直接降低显存需求97%以上,使得MoE训练可在单卡40GB加速器上运行,极具工程价值。

时效性10 ×0.15

论文为最新提交(arXiv 2607),紧贴当前大规模模型训练内存瓶颈的热点。

信源可信度8 ×0.15

arXiv预印本,虽未经同行评审,但方法清晰、实验可复现,作者机构未知。

基础分 92 × T2 信源权重 ×0.9 = 83

精选线 70已入选精选

20:47arXiv AI/CL/LG(论文)T2论文

精选优化器状态应放在哪里?面向内存高效MoE训练的分层状态分配

这篇论文对训练MoE模型的团队非常有价值:它证明了通过精细的优化器状态分配,可以在不牺牲精度的情况下大幅降低显存占用,甚至还能提升性能。尤其适合那些受限于40GB或80GB GPU显存的实验室或企业。值得仔细阅读其分层策略和动量保留的消融分析,可为实际训练框架(如DeepSpeed、Megatron)提供优化方向。

AI论文#AdamW#SkewAdam#分层状态分配
83

五维评分

信息增量10 ×0.3

首次系统解决MDLM中强化学习因忽略掩码顺序而导致的近似问题,填补了理论空白。

内容质量9 ×0.2

推导严谨,实验设置合理,结果显著,论文结构清晰,但未包含消融分析细节且未见开源代码。

实用价值8 ×0.2

提供可直接实现的梯度公式,对从事扩散模型RL训练的团队有明确指导意义,但通用性需更多验证。

时效性10 ×0.15

预印本于2026年7月发布,正值扩散语言模型与RL结合的研究热点,时机极佳。

信源可信度9 ×0.15

来源于arXiv官方预印本平台,作者所属机构为知名高校/实验室,但尚未经同行评审。

基础分 92.5 × T2 信源权重 ×0.9 = 83

精选线 70已入选精选

00:57arXiv AI/CL/LG(论文)T2论文

精选掩码感知策略梯度用于扩散语言模型

这篇论文的价值在于它精准点出了扩散语言模型在RL训练中被长期忽视的‘掩码顺序’问题,并给出了优雅的理论解。对于正在探索扩散模型后训练或推理增强的团队,这篇文章比单纯调优采样的工作更具根本意义。它证明了‘选什么’和‘什么时候选’必须一起优化,这很可能也会启发其他扩散生成任务。

AI论文#GSM8K#MBPP#强化学习

引用前请核对原文