What Cursor is really emphasizing here is speed — they’re claiming it runs about four times faster than GPT-5/Sonnet, while still offering roughly the same level of performance.
The metrics in the post seem quite abstract. Does anyone know the detailed metrics of this mysterious model? Was it fine-tuned from open models or trained from scratch?
While your engineering perspective emphasizes efficiency, it's worth noting that, akin to the human brain, we aim to develop powerful LLMs capable of performing complex cognitive tasks. Although they may operate more slowly, these models can, for instance, reason through intricate problems without external tools, much like Einstein conceptualized relativity through thought experiments or Andrew Wiles proved Fermat's Last Theorem through deep mathematical insight
I completely agree with the point made here.
Apart from the research controversial in the paper, however, from an engineering practice perspective, the methodology presented in the paper offers the industry an effective approach to distill structural cognitive capabilities from advanced models and integrate them into less competent ones.
Moreover, I find the Less-Is-More Reasoning (LIMO) hypothesis particularly meaningful. It suggests that encoding the cognitive process doesn't require extensive data; instead, a small amount of data can elicit the model's capabilities. This hypothesis and observation, in my opinion, are highly significant and offer valuable insights, much more than the specific experiment itself.
After reviewing the paper and GitHub training dataset, I have the following observations:
The 800+ training samples, each containing solutions with detailed reasoning steps, were primarily generated by DeepSeek r1 and advanced models. The reasoning processes within these training solutions are crucial. It's possible that the advanced models have encoded these reasoning processes through the generated samples. Given a sufficiently large model, it can effectively restore such reasoning weights, effectively adding a delta from DeepSeek r1, among others.
Therefore, it's not surprising that, with relatively few fine-tuning data, Qwen 2.5 has achieved such significant improvements.
This is merely a conjecture. Further research is needed to analyze and visualize the changes in network weights before and after fine-tuning.
This is a promising trial of an innovative pricing model. Many AI products require a $19.9 subscription fee just to try them out, yet I only use most of them a few times a month. For such occasional use, a monthly subscription doesn't seem very practical or user-friendly. I hope AI products eventually move to a usage-based charging model.
Sign up for an API account and connect something like Open WebUI[0] and you can have just that, with a few caveats (mostly around specific UI features).
Bonus is you can query multiple models at once, including local llama.cpp/Ollama models. I use it with the Claude and OpenAI APIs, as well as local Mistral, Qwen, and DeepSeek models.
I totally uphold your idea. One more case, machine learning, especially deep learning, which lacks strict mathematical model and theoretical explanation, actually works pretty well on reality problems in fact.
Maybe, it takes years for theorists to build a theoretical framework to explain, but it does not hinder us to exploit the technique.
