Kimi Researcher: Advancing AI Agents with End-to-End Reinforcement Learning
Kimi Researcher is the flagship product of the Kimi Agent initiative, designed to revolutionize research automation through advanced AI agents. Leveraging end-to-end reinforcement learning (RL), Kimi Researcher generates comprehensive, well-cited reports exceeding 10,000 words and has achieved a state-of-the-art (SOTA) score of 26.9% on the challenging Humanity's Last Exam (HLE) benchmark. Notably, it is the first large model AI agent developed entirely using end-to-end RL.
Why End-to-End Reinforcement Learning for AI Agents?
Unlike traditional search tools, Kimi Researcher is built to conduct genuine research. The end-to-end reinforcement learning approach is critical for developing advanced AI agents capable of autonomous reasoning and continuous improvement.
Limitations of Traditional Methods
Conventional techniques like prompt engineering and supervised fine-tuning (SFT) rely on human-designed rules and annotated data, which limit scalability. In contrast, reinforcement learning enables autonomous exploration, allowing AI agents to learn and adapt beyond preset instructions.
How Kimi Researcher Uses Reinforcement Learning
In Kimi Researcher's RL framework, the AI agent operates in a controlled digital environment, learning research skills through trial and error. Key advantages include:
- Dynamic Strategy Generation: RL agents adapt strategies dynamically, discovering creative solutions to complex problems and allowing for seamless upgrades to the base model.
- Scalability: Training environments can be expanded with more examples, enabling the model to improve autonomously with increased data and compute resources.
- Self-Improving Data Generation: RL allows the agent to generate its own training data, refining its abilities through ongoing exploration and a reliable reward signal. (See also: Rich Sutton's "The Bitter Lesson".)
Kimi Researcher's Performance on AI Benchmarks
Kimi Researcher's reinforcement learning approach has produced significant results. On the Humanity's Last Exam (HLE) benchmark, it improved its score from 8.6% to 26.9%, demonstrating the effectiveness of RL in training advanced AI agents. For comparison, OpenAI's Deep Research team reported a similar increase from 20% to 26.6% on related tasks.
Additionally, Kimi Researcher achieved a pass@4 metric of 40.17%, indicating a 40%+ chance of solving difficult problems within four attempts. This metric showcases the agent's ability to internalize successful strategies and continuously enhance its skills.
Emergent intelligent behaviors have also been observed, with Kimi Researcher independently developing strategies to complete complex tasks—evidence that end-to-end RL is a promising path toward general artificial intelligence.
What Can You Do with Kimi Researcher?
Kimi Researcher empowers users to quickly understand new fields by generating in-depth, cited reports. It supports academic paper analysis, literature reviews, and serves as a personal research copilot for information gathering and analysis.
Example Use Cases of Kimi Researcher
1. Identifying Advanced Benchmarks:
Prompt: Survey all advanced benchmarks that all frontier LLM scores lower than 20%, focus on text. Example like HLE
Kimi Researcher identified new benchmarks such as AGI-2, HLE, OlympiadBench, FrontierMath, and Seal QA.
2. Clarifying Historical Narratives:
Prompt: Analyze the evolution of the three major monetary systems in human history: the Gold Standard, the Bretton Woods system, and the floating exchange rate system.
Kimi Researcher organizes key events and systemic differences along a timeline for quick understanding.
3. Rapid Domain Understanding:
Prompt: I'm an in-house lawyer at an international robotics company, and the management is considering expanding into Southeast Asian countries. However, I'm not quite confident about the data and privacy requirements in those countries. Could you help me list the names of the data and privacy laws of Southeast Asian countries (on a country-by-country basis), and preferably provide a brief summary and key takeaways of those laws?
In just over ten minutes, Kimi Researcher generated a 10,000-word report comparing key regulations across ten countries. The interactive report format makes regulatory comparison straightforward.
4. Analyzing Fictional Character Skills:
Prompt: Analyze the actual abilities of the main players from each team in Slam Dunk based on their basketball skill panels, and provide a scouting report.
5. Consumer Research and Product Analysis:
Prompt: I've been thinking about getting a portable blender recently, mainly to quickly make a glass of juice or a meal replacement shake for breakfast. But I've noticed the market is flooded with all kinds of these blenders, and the prices vary wildly. Some cost only a few dollars, while others go for over $50. Their feature descriptions all sound pretty similar, mentioning things like 'magnetic charging,' 'one-touch start,' and 'quiet high-speed motor.' Could you explain this from an industry insider's perspective? Why is there such a big price difference for portable blenders with similar features? Which advertised features are actually useful, and which are just marketing gimmicks? Within a budget of around $20, are there any reliable, high-quality models you'd recommend? I'm hoping for a detailed analysis to help me avoid making a bad purchase.
The Future of AI Agents with Reinforcement Learning
Kimi Researcher marks a significant milestone in the evolution of capable AI agents. Reinforcement learning is a key driver in transforming AI from simple tools into true research partners, enabling deep collaboration and continuous improvement.