Summer 2025 Engineering Intern

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More. Researchers at Together AI and Agentica have released DeepCoder-14B, a new coding model that delivers impressive performance comparable to leading proprietary models like OpenAI’s o3-mini. Built on top of DeepSeek-R1, this model gives more flexibility to integrate high-performance code generation and reasoning capabilities into real-world applications. Importantly, the teams have fully open-sourced the model, its training data, code, logs and system optimizations, which can help researchers improve their work and accelerate progress.Competitive coding capabilities in a smaller packageThe research team’s experiments show that DeepCoder-14B performs strongly across several challenging coding benchmarks, including LiveCodeBench (LCB), Codeforces and HumanEval+.“Our model demonstrates strong performance across all coding benchmarks… comparable to the performance of o3-mini (low) and o1,” the researchers write in a blog post that describes the model.Interestingly, despite being trained primarily on coding tasks, the model shows improved mathematical reasoning, scoring 73.8% on the AIME 2024 benchmark, a 4.1% improvement over its base model (DeepSeek-R1-Distill-Qwen-14B). This suggests that the reasoning skills developed through RL on code can be generalized effectively to other domains.Credit: Together AIThe most striking aspect is achieving this level of performance with only 14 billion parameters. This makes DeepCoder significantly smaller and potentially more efficient to run than many frontier models.Innovations driving DeepCoder’s performanceWhile developing the model, the researchers solved some of the key challenges in training coding models using reinforcement learning (RL).The first challenge was curating the training data

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn MoreDeep Cogito, a new AI research startup based in San Francisco, officially emerged from stealth today with Cogito v1, a new line of open source large language models (LLMs) fine-tuned from Meta’s Llama 3.2 and equipped with hybrid reasoning capabilities — the ability to answer quickly and immediately, or “self-reflect” like OpenAI’s “o” series and DeepSeek R1.The company aims to push the boundaries of AI beyond current human-overseer limitations by enabling models to iteratively refine and internalize their own improved reasoning strategies. It’s ultimately on a quest toward developing superintelligence — AI smarter than all humans in all domains — yet the company says that “All models we create will be open sourced.”Deep Cogito’s CEO and co-founder Drishan Arora — a former Senior Software Engineer at Google who says he led the large language model (LLM) modeling for Google’s generative search product —also said in a post on X they are “the strongest open models at their scale – including those from LLaMA, DeepSeek, and Qwen.”The initial model lineup includes five base sizes: 3 billion, 8 billion, 14 billion, 32 billion, and 70 billion parameters, available now on AI code sharing community Hugging Face, Ollama and through application programming interfaces (API) on Fireworks and Together AI.They’re available under the Llama licensing terms which allows for commercial usage — so third-party enterprises could put them to work in paid products — up to 700 million monthly users, at which point they need to obtain a paid license from Meta.The company plans to release even larger models — up to 671 billion parameters — in the coming months.Arora describes the company’s training approach, iterated distillation and amplification (IDA), as a novel alternative to traditional reinforcement learning from human feedback (RLHF) or teacher-model distillation.The core idea behind IDA is to allocate more compute for a model to generate improved solutions, then distill the improved reasoning process into the model’s own parameters — effectively creating a feedback loop for capability growth. Arora likens this approach to Google AlphaGo’s self-play strategy, applied to natural language.The Cogito models are open-source and available for download via Hugging Face and Ollama, or through APIs provided by Fireworks AI and Together AI. Each model supports both a standard mode for direct answers and a reasoning mode, where the model reflects internally before responding.Benchmarks and evaluationsThe company shared a broad set of evaluation results comparing Cogito models to open-source peers across general knowledge, mathematical reasoning, and multilingual tasks. Highlights include:Cogito 3B (Standard) outperforms LLaMA 3.2 3B on MMLU by 6.7 percentage points (65.4% vs