News from the AI & ML world
DeeperML
Alyssa Mazzina@RunPod Blog
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Deep Cogito, a new San Francisco-based AI company, has emerged from stealth with the release of Cogito v1, a family of open-source large language models (LLMs) ranging from 3B to 70B parameters. These models are trained using Iterated Distillation and Amplification (IDA), a novel technique aimed at achieving artificial superintelligence (ASI). Cogito v1 models are available on Hugging Face, Ollama, and through APIs on Fireworks and Together AI under the Llama licensing terms, allowing for commercial usage up to 700 million monthly users. The company plans to release even larger models, up to 671 billion parameters, in the coming months.
IDA, central to the Cogito v1 release, is described as a scalable and efficient alignment strategy for ASI using iterative self-improvement. It involves amplifying model capabilities through increased computation to derive better solutions and distilling these amplified capabilities back into the model's parameters. Founder Drishan Arora, formerly of Google, states that this creates a positive feedback loop, allowing the model's intelligence to scale more directly with computational resources, instead of being limited by human or larger model supervision. This approach is likened to Google AlphaGo’s self-play strategy, but applied to natural language models.
Cogito v1 models support two distinct modes: Direct Mode for fast, high-quality completions for common tasks, and Reasoning Mode for slower but more thoughtful responses using added compute. The 70B model, trained on RunPod using H200 GPUs, outperforms LLaMA 3.3 70B and even the 109B LLaMA 4 Scout model across major benchmarks. According to Arora, the models are the "strongest open models at their scale," outperforming alternatives from LLaMA, DeepSeek, and Qwen. Deep Cogito claims that the models were developed by a small team in approximately 75 days, highlighting IDA’s potential scalability.
ImgSrc: blog.runpod.io
References :
IDA, central to the Cogito v1 release, is described as a scalable and efficient alignment strategy for ASI using iterative self-improvement. It involves amplifying model capabilities through increased computation to derive better solutions and distilling these amplified capabilities back into the model's parameters. Founder Drishan Arora, formerly of Google, states that this creates a positive feedback loop, allowing the model's intelligence to scale more directly with computational resources, instead of being limited by human or larger model supervision. This approach is likened to Google AlphaGo’s self-play strategy, but applied to natural language models.
Cogito v1 models support two distinct modes: Direct Mode for fast, high-quality completions for common tasks, and Reasoning Mode for slower but more thoughtful responses using added compute. The 70B model, trained on RunPod using H200 GPUs, outperforms LLaMA 3.3 70B and even the 109B LLaMA 4 Scout model across major benchmarks. According to Arora, the models are the "strongest open models at their scale," outperforming alternatives from LLaMA, DeepSeek, and Qwen. Deep Cogito claims that the models were developed by a small team in approximately 75 days, highlighting IDA’s potential scalability.
ImgSrc: blog.runpod.io
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References :
- RunPod Blog: Built on RunPod: How Cogito Trained High-Performance Open Models on the Path to ASI
- AI News | VentureBeat: New open source AI company Deep Cogito releases first models and they’re already topping the charts
- AI News: Deep Cogito open LLMs use IDA to outperform same size models
- www.artificialintelligence-news.com: Deep Cogito open LLMs use IDA to outperform same size models
Classification:
- HashTags: #OpenSourceAI #HighPerformanceAI #ArtificialSuperintelligence
- Company: RunPod, Cogito
- Target: AI Researchers, Developers
- Product: Cogito v1
- Feature: Open-Source AI Models
- Type: AI
- Severity: Informative