Nikita Morozov
56 posts
@nvimorozov
PhD student at @CS_HSE, researcher at @bayesgroup Previously intern at @EPFL, @yandex ICPC World Finalist Generative models, Sampling, RL, AI4Science
We’re releasing UNI-D², a unified codebase for discrete diffusion language models 🤝🚀 Co-led with @vincentpaulinef and an amazing advisor team: @stefanAbauer, @AlexanderTong7 , @andrea_dittadi, @AMK6610, @KaplFer 🙌 🔗 GitHub: github.com/nkalyanv99/UNI… 📚 Docs: nkalyanv99.github.io/UNI-D2/ Reproduce and extend state-of-the-art baselines with one toolkit. Let’s move beyond autoregressive models and push discrete diffusion together 🧵👇
We’re excited to introduce ShinkaEvolve: An open-source framework that evolves programs for scientific discovery with unprecedented sample-efficiency. Blog: sakana.ai/shinka-evolve/ Code: github.com/SakanaAI/Shink… Like AlphaEvolve and its variants, our framework leverages LLMs to find state-of-the-art solutions to complex problems, but using orders of magnitude fewer resources! Many evolutionary AI systems are powerful but act like brute-force engines, burning thousands of samples to find good solutions. This makes discovery slow and expensive. We took inspiration from the efficiency of nature. ‘Shinka’ (進化) is Japanese for evolution, and we designed our system to be just as resourceful. On the classic circle packing optimization problem, ShinkaEvolve discovered a new state-of-the-art solution using only 150 samples. This is a big leap in efficiency compared to previous methods that required thousands of evaluations. We applied ShinkaEvolve to a diverse set of hard problems with real-world applications: 1/ AIME Math Reasoning: It evolved sophisticated agentic scaffolds that significantly outperform strong baselines, discovering an entire Pareto frontier of solutions trading performance for efficiency. 2/ Competitive Programming: On ALE-Bench (a benchmark for NP-Hard optimization problems), ShinkaEvolve took the best existing agent's solutions and improved them, turning a 5th place solution on one task into a 2nd place leaderboard rank in a competitive programming competition. 3/ LLM Training: We even turned ShinkaEvolve inward to improve LLMs themselves. It tackled the open challenge of designing load balancing losses for Mixture-of-Experts (MoE) models. It discovered a novel loss function that leads to better expert specialization and consistently improves model performance and perplexity. ShinkaEvolve achieves its remarkable sample-efficiency through three key innovations that work together: (1) an adaptive parent sampling strategy to balance exploration and exploitation, (2) novelty-based rejection filtering to avoid redundant work, and (3) a bandit-based LLM ensemble that dynamically picks the best model for the job. By making ShinkaEvolve open-source and highly sample-efficient, our goal is to democratize access to advanced, open-ended discovery tools. Our vision for ShinkaEvolve is to be an easy-to-use companion tool to help scientists and engineers with their daily work. We believe that building more efficient, nature-inspired systems is key to unlocking the future of AI-driven scientific research. We are excited to see what the community builds with it! Learn more in our technical report: arxiv.org/abs/2509.19349
The speedrun is over: I defended my PhD this week and became a doctor in applied mathematics (unofficially: in reinforcement learning)! Huge thanks to my supervisors (Eric & Gilles), collaborators, and friends for all the support.
