1 Introduction

本文为论文《Kimi K2：面向智能体能力的开源大模型》「论文原文」板块的对应章节，按 arXiv 官方全文逐字保留，层级与「中文全文翻译」板块一一对应。

The development of Large Language Models (LLMs) is undergoing a profound paradigm shift towards Agentic Intelligence – the capabilities for models to autonomously perceive, plan, reason, and act within complex and dynamic environments. This transition marks a departure from static imitation learning towards models that actively learn through interactions, acquire new skills beyond their training distribution, and adapt behavior through experiences [63]. It is believed that this approach allows an AI agent to go beyond the limitation of static human-generated data, and acquire superhuman capabilities through its own exploration and exploitation. Agentic intelligence is thus rapidly emerging as a defining capability for the next generation of foundation models, with wide-ranging implications across tool use, software development, and real-world autonomy.

Achieving agentic intelligence introduces challenges in both pre-training and post-training. Pre-training must endow models with broad general-purpose priors under constraints of limited high-quality data, elevating token efficiency—learning signal per token—as a critical scaling coefficient. Post-training must transform those priors into actionable behaviors, yet agentic capabilities such as multi-step reasoning, long-term planning, and tool use are rare in natural data and costly to scale. Scalable synthesis of structured, high-quality agentic trajectories, combined with general reinforcement learning (RL) techniques that incorporate preferences and self-critique, are essential to bridge this gap.

In this work, we introduce Kimi K2, a 1.04 trillion-parameter Mixture-of-Experts (MoE) LLM with 32 billion activated parameters, purposefully designed to address the core challenges and push the boundaries of agentic capability. Our contributions span both the pre-training and post-training frontiers:

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We present MuonClip, a novel optimizer that integrates the token-efficient Muon algorithm with a stability-enhancing mechanism called QK-Clip. Using MuonClip, we successfully pre-trained Kimi K2 on 15.5 trillion tokens without a single loss spike.

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We introduce a large-scale agentic data synthesis pipeline that systematically generates tool-use demonstrations via simulated and real-world environments. This system constructs diverse tools, agents, tasks, and trajectories to create high-fidelity, verifiably correct agentic interactions at scale.

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We design a general reinforcement learning framework that combines verifiable rewards (RLVR) with a self-critique rubric reward mechanism. The model learns not only from externally defined tasks but also from evaluating its own outputs, extending alignment from static into open-ended domains.

Kimi K2 demonstrates strong performance across a broad spectrum of agentic and frontier benchmarks. It achieves scores of 66.1 on Tau2-bench, 76.5 on ACEBench (en), 65.8 on SWE-bench Verified, and 47.3 on SWE-bench Multilingual, outperforming most open- and closed-weight baselines under non-thinking evaluation settings, closing the gap with Claude 4 Opus and Sonnet. In coding, mathematics, and broader STEM domains, Kimi K2 achieves 53.7 on LiveCodeBench v6, 27.1 on OJBench, 49.5 on AIME 2025, and 75.1 on GPQA-Diamond, further highlighting its capabilities in general tasks. On the LMSYS Arena leaderboard (July 17, 2025)44 4

https://lmarena.ai/leaderboard/text, Kimi K2 ranks as the top 1 open-source model and 5th overall based on over 3,000 user votes.

To spur further progress in Agentic Intelligence, we are open-sourcing our base and post-trained checkpoints, enabling the community to explore, refine, and deploy agentic intelligence at scale.