INTELLECT-3.1 Brings Open-Source RL Reasoning to 106B MoE Scale
Overview
INTELLECT-3.1 is a 106 billion parameter Mixture-of-Experts reasoning model built by PrimeIntellect as continued training of the base INTELLECT-3 model. The model receives additional reinforcement learning optimization for math, coding, software engineering, and agentic tasks using the prime-rl framework and verifiers library. This is a text-to-text generation model licensed under MIT, making it fully open-source and permissively licensed. The architecture uses a sparse MoE design to achieve the 100B+ parameter scale while remaining computationally efficient compared to dense models of equivalent capability. The model was trained with large-scale reinforcement learning using verifiable rewards, which distinguishes it from standard supervised fine-tuned models and targets improved performance on reasoning-heavy and tool-use tasks. To run this model, you need the transformers library and can serve it via vLLM, which is the recommended inference framework.
Best use cases
Mathematical problem solving and verification.INTELLECT-3.1 receives explicit reinforcement learning training on mathematical tasks with...
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