New Language Model R1 Catches Up to OpenAI’s o1, But With a Catch
Unconventional Approach Yields Improved Performance
Unlike conventional Large Language Models (LLMs), these new models, known as Specialized Reasoners (SR), take extra time to produce responses. This extra time often increases performance on tasks involving math, physics, and science. The latest open model, R1, is generating significant attention for its ability to catch up to OpenAI’s o1.
Impressive Performance in Benchmarks
For example, DeepSeek reports that R1 outperformed OpenAI’s o1 on several benchmarks and tests, including AIME (a mathematical reasoning test), MATH-500 (a collection of word problems), and SWE-bench Verified (a programming assessment tool). As with any AI benchmark, these results have yet to be independently verified.
A Chart of DeepSeek R1 Benchmark Results
[A chart of DeepSeek R1 benchmark results, created by DeepSeek]
Multiple Chinese Labs Release Competitor Models
TechCrunch reports that three Chinese labs—DeepSeek, Alibaba, and Moonshot AI’s Kimi—have now released models that match o1’s capabilities, with DeepSeek first previewing R1 in November.
A Catch: Censorship and Filtering
The new DeepSeek model comes with a catch. If run in the cloud-hosted version, R1 will not generate responses about certain topics, such as Tiananmen Square or Taiwan’s autonomy, as it must "embody core socialist values," according to Chinese Internet regulations. This filtering comes from an additional moderation layer that isn’t an issue if the model is run locally outside of China.
Implications and Wider Implications
Dean Ball, an AI researcher at George Mason University, wrote on X, "The impressive performance of DeepSeek’s distilled models (smaller versions of r1) means that very capable reasoners will continue to proliferate widely and be runnable on local hardware, far from the eyes of any top-down control regime."
Conclusion
The introduction of R1 and other similar models has significant implications for the development of AI and its applications. With their improved performance and potentially more accessible, these models may lead to a wider range of possibilities for AI-powered applications.
Frequently Asked Questions
Q: What is the difference between LLMs and SR models?
A: SR models, like R1, take extra time to produce responses, which improves their performance on tasks involving math, physics, and science.
Q: How do these models compare to OpenAI’s o1?
A: R1 has reportedly outperformed o1 on certain benchmarks and tests, but the results have not been independently verified.
Q: What are the potential implications of these models?
A: The widespread adoption of these models could lead to a range of AI-powered applications and innovations, but it also raises concerns about censorship and control.
