The development of Kimi k1.5 involved a multi-stage process, including pretraining, supervised fine-tuning (SFT), and reinforcement learning (RL). The model's training methodology focuses on effective RL scaling and multimodal integration, achieving exceptional results without relying on complex techniques such as Monte Carlo tree search, value functions, or process reward models. This simplicity in approach has proven highly effective, allowing Kimi k1.5 to excel in both long and short chain-of-thought (CoT) reasoning tasks.
One of the key innovations in Kimi k1.5 is its ability to handle long context scaling, with the model capable of processing context lengths of up to 128k tokens during RL generation. This expanded context window allows the model to tackle more complex and nuanced tasks, improving its performance across a wide range of applications. The team behind Kimi k1.5 employed partial rollouts to enhance training efficiency, reusing large portions of previous trajectories to avoid the computational cost of generating entirely new ones for each iteration.
Kimi k1.5 shines particularly bright in its short-CoT performance, vastly outperforming state-of-the-art models like GPT-4o and Claude Sonnet 3.5 in math, coding, vision, and multimodal tasks. The performance margins in some cases reach up to 550%, showcasing the model's exceptional efficiency and capability in providing concise, accurate responses.
The multimodal nature of Kimi k1.5 sets it apart from many competitors. The model can process both text and images, allowing it to draw conclusions across different types of input. This capability has led to impressive scores on multimodal benchmarks such as MathVista and MMMU, demonstrating the model's versatility in handling complex, multi-format information.
Moonshot AI has developed two versions of Kimi k1.5 - a long-CoT version for detailed reasoning and a short-CoT version for concise answers. The long-CoT version excels at walking through its thinking process step by step, while the short-CoT version aims for brevity without sacrificing accuracy. Both versions have shown remarkable performance across various benchmarks, often matching or exceeding the capabilities of leading models in the field.
As of January 25, 2025, Moonshot AI has made Kimi k1.5 available to the public through a free web version at Kimi.ai. This release includes support for English language interactions, though the company notes that language support is still being fine-tuned. The web version offers access to the full feature set of k1.5 without usage limits, including real-time web search across more than 100 websites, the ability to process up to 50 files simultaneously, and improved reasoning and image understanding capabilities.
Key features of Kimi k1.5 include:
- State-of-the-art performance in both long and short chain-of-thought reasoning tasks
- Multimodal capabilities, processing both text and images
- Long context scaling with up to 128k tokens
- Improved policy optimization for robust learning
- Simplistic yet effective reinforcement learning framework
- Exceptional performance on complex benchmarks like AIME, MATH 500, Codeforces, and MathVista
- Efficient long-to-short context training methodology
- Real-time web search capabilities across numerous websites
