INTELLECT-3：技術報告

摘要

我們推出INTELLECT-3——一個基於端到端強化學習基礎設施棧訓練的1060億參數混合專家模型（活躍參數120億）。該模型在數學、編程、科學和推理基準測試中，以同等規模實現了最先進的性能表現，超越許多參數量更大的前沿模型。我們將開源該模型及其完整創建基礎設施棧，包括強化學習框架、完整訓練方案，以及通過驗證器庫構建、來自環境中心社區平台的豐富訓練評估環境集。為此我們同步推出prime-rl開放框架，這款專為大規模異步強化學習設計的框架可實現從單節點到數千張GPU的無縫擴展，並針對智能體強化學習特性提供多輪交互與工具使用的原生支持。基於該技術棧，我們在GLM-4.5-Air-Base模型基礎上同步開展SFT與RL訓練，成功將強化學習訓練擴展至512張H200 GPU並保持高訓練效率。

English

We present INTELLECT-3, a 106B-parameter Mixture-of-Experts model (12B active) trained with large-scale reinforcement learning on our end-to-end RL infrastructure stack. INTELLECT-3 achieves state of the art performance for its size across math, code, science and reasoning benchmarks, outperforming many larger frontier models. We open-source the model together with the full infrastructure stack used to create it, including RL frameworks, complete recipe, and a wide collection of environments, built with the verifiers library, for training and evaluation from our Environments Hub community platform. Built for this effort, we introduce prime-rl, an open framework for large-scale asynchronous reinforcement learning, which scales seamlessly from a single node to thousands of GPUs, and is tailored for agentic RL with first-class support for multi-turn interactions and tool use. Using this stack, we run both SFT and RL training on top of the GLM-4.5-Air-Base model, scaling RL training up to 512 H200s with high training efficiency.

INTELLECT-3：技術報告

INTELLECT-3: Technical Report

摘要

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