<p><strong>SAN FRANCISCO</strong> &#8212; <a href="https://cts.businesswire.com/ct/CT?id=smartlink&;url=http%3A%2F%2Fdeepcogito.com&;esheet=54594415&;newsitemid=20260826913379&;lan=en-US&;anchor=Deep+Cogito&;index=1&;md5=99e64a0746876d3627d8d98e805abb2f" target="_blank" rel="nofollow noopener" shape="rect"><b>Deep Cogito</b></a>, a post-training research lab focused on reinforcement learning and self-improvement, has landed a $43 million Series A led by TQ Ventures, with participation from Benchmark, Nexus Venture Partners, Atreides Management, South Park Commons, and Zscaler. The round brings Deep Cogito&#8217;s total funding to more than $56 million.</p>
<p>Deep Cogito was founded by <b>Drishan Arora and Dhruv Malrana</b>, who previously helped build Google&#8217;s AI Search products, including AI Mode and AI Overviews. At Google, Arora led Gemini post-training for AI Search, while Malrana led its product from inception.</p>
<p>They started the company around a single thesis: as AI advances, most of the frontier will be determined by post-training, the process that turns a pre-trained model into a capable reasoner and teaches it to improve on increasingly difficult tasks.</p>
<p>The company&#8217;s research focuses on large-scale reinforcement learning and recursive self-improvement. One of its research directions, <b>Iterated Distillation and Amplification (IDA)</b>, repeatedly allows a model to use additional computation to produce answers beyond what it could generate directly, then distills those improvements back into the model&#8217;s weights. The long-term goal is to build models that progressively improve their own capabilities and ultimately move beyond the limits of human-generated training data.</p>
<p>&#8220;Pre-training gives a model an enormous amount of knowledge and capability. Post-training determines what that model can actually become,&#8221; said <b>Drishan Arora, co-founder and CEO of Deep Cogito</b>. &#8220;We believe the next frontier is in finding ways for models to improve their own intelligence, internalize those improvements, and become increasingly capable over time.&#8221;</p>
<p>Deep Cogito first developed its post-training methods through its open-weight model releases. Across model sizes from 3B to 600B+, that work showed the company could improve strong models through post-training and reinforcement learning. The same system now powers the platform Deep Cogito is making available to companies that want to build specialized intelligence for their own products.</p>
<p>&#8220;Very few teams outside the largest AI labs have demonstrated the ability to post-train models at this scale,&#8221; said <b>Schuster Tanger, Co-Founding Partner at TQ Ventures</b>. &#8220;Deep Cogito has done that in public through its model releases, and is now bringing the same capability to companies that want intelligence built around their own products. We believe that combination of frontier research and real-world deployment is extremely powerful.&#8221;</p>
<p><b>Zscaler</b>, a leader in cloud security, began working with Deep Cogito as a customer and is also participating in the Series A as a strategic investor.</p>
<p>&#8220;Frontier models were useful, but they were not enough for the level of specialization we needed,&#8221; said <b>Dhawal Sharma, Executive Vice President of AI Security and Strategic Initiatives at Zscaler</b>. &#8220;Deep Cogito stood out because they went deeper than lightweight customization. They worked closely with us to understand our products and the metrics we care about and helped train that intelligence into the model itself.&#8221;</p>
<p>Deep Cogito will use the new capital to expand its research and engineering team, scale the infrastructure required to train frontier models, advance future Cogito releases, and grow its work with enterprises building specialized intelligence on proprietary data.</p>

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