Exciting developments from our lab at Avataar! We've been exploring ways to compress domain-specific, task-driven intelligence into ultra-efficient models—some as small as 135M parameters—without compromising performance. Early results using “solving differential polynomials” as the task, indicate that these models can rival OpenAI's o1 in accuracy while achieving ~580x lower inference costs. The cultivation of generalized reasoning, building systems capable of autonomously acquiring expertise across a boundless spectrum of domains and tasks, has emerged as the way forward to achieving AGI-level generalized reasoning complexity, and recently evidenced in Competitive Programming by OpenAI’s soon-to-be-released o3. We are operating on an orthogonal axis of unlocking efficiency, where one can distill a given state-of-the-art generalized reasoning model performance into compact footprints that serve specific task-oriented agents in real-world applied use-cases. The potential here aligns closely with the evolution of #AgenticAI, where workflows rely on ensembles of efficient & specialized agents, enabling near-instant inference at a fraction of traditional #LLM costs. Our progress is built on a lightweight yet powerful distillation approach—leveraging LLMs as ‘teachers’ to train smaller, task-specialized ‘students’, via supervised fine-tuning and reinforcement learning using synthetically generated training data sets (with rich chain-of-thought reasoning). We’re just getting started and these are early results. Future research directions we’re keen to explore: • Enhancing reasoning within tiny models for real-world decision-making • Improving generalization and adaptability across diverse tasks, leveraging synthetic dataset creation from unstructured data • Scaling this approach to unlock even greater efficiency across industries via a mixture-of-experts strategy using ensembles of multiple tiny specialized models. What’s even more exciting? These models can soon aspire to run on the edge in the future, making on-premise AI deployment in enterprises more viable than ever. For the technically curious, you can find our code, benchmarks, and initial findings in the thread!