Kimi K2 Technical Report Officially Released: "Nanny Level" In-Depth Analysis, Read All the Secrets of Trillion Parameter Intelligence in One Article

The kimi k2 tech report is finally here, and it's probably the most worthwhile tech report in recent memory. It's full of goodwill, and it's even better with Manus's lessons learned on building Agents, which was released two days ago.

In a nutshell: Moonshot team used 1 trillion+ parameter sparse MoE architecture + MuonClip stable training + massive Agentic data + "verifiable reward × self-criticism" joint RL to build a generalized large model with performance closest to Claude-Opus in the open source camp.

Report Address:

localization

Kimi The goal of K2 is clear: to advance LLM from "passive dialog" to the Agentic stage of "active planning, execution and self-correction". To this end, the team made systematic engineering improvements around the three main lines of "training stability, tool usage capability and RL alignment".

Three core technology breakthroughs

pre-training

Pre-training is the foundation of modeling capability. In the context of increasing scarcity of high quality data, how to improve the "learning efficiency" of each Token and ensure the stability of ultra-large-scale training is the primary challenge faced by Kimi K2.

1. MuonClip optimizer

Large model training, especially with the efficient but more "aggressive" Muon optimizer, often encounters training instability due to "attention logit explosion".

To solve this problem, instead of adopting the "hard" method of directly cropping logit, the team proposed a novel weight cropping mechanism - theQK-Clip. The core idea is:

Post hoc intervention: during training, when the logit value of an attention head exceeds a preset threshold τ, it does not directly intervene in the current computation

Signal-driven: the suprathreshold is used as a signal to scale the query (Query) and key (Key) projection weight matrices (Wq, Wk) of this header equally after parameter update

Fine-grained control: this scaling is done "per-head", affecting only the attention head of the problem, minimizing interference with the model training dynamics.

By combining QK-Clip with the Muon optimizer into the new MuonClip, Kimi K2 succeeded in pre-training 15.5 trillion tokens in theZero "loss spike". of extreme stability. This creates a strong and reliable foundation for all subsequent competence development

2. Data "rephrasing": squeezing every drop of value out of high-quality data

Simply increasing the amount of data is not a long-term solution.Kimi K2 introduces Rephrasing, an innovative synthetic data generation strategy designed to amplify the value of high-quality data, rather than simply repeating training.

Knowledge data retelling: For knowledge-intensive texts, use stylistically diverse cues to guide the large model to reorganize and express the original text in different perspectives and styles. This is equivalent to letting the model learn the same knowledge point "in a different way", which not only strengthens memory but also avoids overfitting.

Math data retelling: rewriting high-quality math documents in "study notes" style and translating math materials in other languages enhances the model's assimilation of math concepts and solutions.

The experiment proves that compared to simply repeating the original data for 10 epochs, the data that has been retold 10 times in different ways can improve the model's accuracy on SimpleQA from 23.76% to 28.94%, which is a significant effect.

Model Architecture and Systems Engineering:

Kimi K2's architectural design is innovative in its inheritance and extremely engineered and optimized at the system level to balance performance and cost.

Architecture choice: An ultra-sparse MoE architecture similar to DeepSeek-V3 and Multiple Latent Attention (MLA) are used. However, Kimi K2 is much sparser, with 384 experts (256 for DeepSeek-V3) and 8 activations per forward propagation. This is based on the finding of its sparsity scaling law: increasing the total number of experts consistently reduces model loss with a constant number of activated parameters

Reasoning Efficiency Consideration: In order to optimize the efficiency of long text reasoning, Kimi K2 cuts the number of attention heads from 128 to 64 in DeepSeek-V3. The team found experimentally that the performance gain from doubling the attention heads (~0.5%-1.2%) outweighs the huge inference overhead (e.g., an increase of 83% FLOPs at 128K contexts) caused by doubling the attention heads compared to the huge inference overhead (e.g., an increase of 83% FLOPs) caused by doubling the attention heads at long sequences. This is a wise tradeoff between performance and efficiency

Parallelism and communication: For training, Kimi K2 utilized a flexible combination of 16-way expert parallelism (EP), pipeline parallelism (PP), and ZeRO-1 data parallelism. A key engineering detail is that the team achieved efficient training throughput by delicately scheduling the time-consuming expert parallel (EP) communication to perfectly overlap with the computational process in a standard 1F1B pipeline, while minimizing the communication overhead by choosing a smaller EP size (16-way)

Post-training core: systematic construction of "intelligent body" capabilities

If pre-training gives Kimi K2 a wealth of knowledge, then the post-training phase is about honing its "knowing and doing" intelligence!

1. Large-scale intelligences data synthesis pipeline

In order for models to learn to use tools to solve complex problems, the Kimi K2 team built a powerful data synthesis system that simulates real-world tool use scenarios. The pipeline is divided into three steps:

Construction and evolution of the tool library: First, more than 3,000 real-world tools from GitHub (MCP protocol) were collected. Then, through a "domain evolution" approach, more than 20,000 synthesized tools covering a wide range of application scenarios are gradually generated from top-level categories such as finance, software, robotics, etc. This ensures the diversity and coverage of the tool library. This ensures the diversity and coverage of the tool library.

Diversified generation of intelligences and tasks: Thousands of "intelligences" with different capabilities, expertise and behavioral patterns are generated for different combinations of tools, and tasks ranging from simple to complex are designed for them. Each task is accompanied by clear success criteria (Rubric).

Simulation and screening of multi-round interaction trajectories: this is the most critical step. The system generates complete trajectories of the interaction of the intelligences with the environment by simulating the user, the environment in which the simulation tool is executed (a world model) and the referee intelligences responsible for the evaluation. Only those trajectories that are judged successful according to the task criteria are retained for training purposes

What's more, Kimi K2 uses a hybrid approach that combines simulation and reality. In fidelity-demanding coding and software engineering tasks, the model executes code in a real sandbox environment and receives feedback, ensuring that the learned capabilities are equally effective in the real world!

2. A generalized reinforcement learning framework: beyond simple right and wrong

Kimi K2's Reinforcement Learning (RL) framework is a major highlight that goes beyond traditional RL that relies only on tasks with explicit answers.

Verifiable Reward (RLVR): For math, logic, code, and other tasks where there is a clear right and wrong, the model receives a direct reward signal from the results of the execution. This data is used to build a "Verifiable Reward Gym" that ensures that the model continues to improve in these hard-core competencies

Self-Critique Rubric Reward: For highly subjective tasks such as creative writing and open-ended quizzes, there is no standard answer. At this point, Kimi K2 will act as a referee, pairwise comparing and scoring multiple answers generated by itself based on a set of internal core values (e.g., clear, objective, helpful, etc.) and task-specific instructions, thus generating a reward signal.

Closed-loop optimization: What's more subtle is that the ability of this "referee" is also iteratively optimized during the RL process. It will use the "objective judgment" learned from the RLVR task to calibrate and improve its own judgment standard on the subjective task, forming a closed loop of ability transfer and self-improvement.

In addition, the RL algorithm introduces Budget Control to avoid generating lengthy responses, PTX loss to prevent forgetting high-quality SFT data, and a temperature decay strategy to balance exploration and utilization, details that together ensure efficient and comprehensive RL training

industry significance

Taking sparse MoE to true scale + open source: 1 T-level model, 32 B activation parameters, developer-friendly

Provides a complete Agent training pipeline: tool specification → data synthesis → multi-source reward → efficient RL, reusable

A New Paradigm for Training Steady State: MuonClip Demonstrates the Feasibility of "Efficient Optimization with Large Steps + Weight Trimming" and Provides a Template for Subsequent Training of Tens of Billion to Trillions of Training

put at the end

Kimi K2's technical report not only demonstrates a powerful trillion-parameter model, but more importantly, it depicts a feasible path to "Open Intelligence" for the industry. From the stable and efficient pre-training method (MuonClip), to the systematic framework for building intelligence capabilities, to the ultimate engineering optimization, every aspect of Kimi K2 is full of in-depth thinking and solid innovation!

By open-sourcing this model, Dark Side of the Moon provides the entire AI community with a high-starting platform for researching and applying cutting-edge intelligent body technology, which will undoubtedly accelerate "AI Agent"Kimi K2 proves that through careful design and systems engineering, open source models are also capable of reaching the top of the world in the field of intelligences, which represent the future of general artificial intelligence.

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