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So-called reasoning AI models are becoming easier \u2014 and cheaper \u2014 to develop. <\/p>\n
On Friday, NovaSky, a team of researchers based out of UC Berkeley\u2019s Sky Computing Lab, released Sky-T1-32B-Preview, a reasoning model that\u2019s competitive with an earlier version of OpenAI\u2019s o1<\/a> on a number of key benchmarks. Sky-T1 appears to be the first truly open source reasoning model in the sense that it can be replicated from scratch<\/a>; the team released the data set they used to train it as well as the necessary training code.<\/p>\n \u201cRemarkably, Sky-T1-32B-Preview was trained for less than $450,\u201d the team wrote in a blog post<\/a>, \u201cdemonstrating that it is possible to replicate high-level reasoning capabilities affordably and efficiently.\u201d<\/p>\n $450 might not sound that affordable. But it wasn\u2019t long ago that the price tag for training a model with comparable performance often ranged in the millions of dollars<\/a>. Synthetic training data, or training data generated by other models, has helped drive costs down. Palmyra X 004, a model recently released by AI company Writer, trained almost entirely on\u00a0synthetic data<\/a>, reportedly cost just $700,000 to develop.<\/p>\n Unlike most AI, reasoning models effectively fact-check themselves, which\u00a0helps them to avoid some of the\u00a0pitfalls\u00a0that normally trip up models<\/a>. Reasoning models take a little longer \u2014 usually seconds to minutes longer \u2014 to arrive at solutions compared to a typical non-reasoning model. The upside is, they tend to be more reliable in domains such as physics, science, and mathematics.<\/p>\n The NovaSky team says it used another reasoning model, Alibaba\u2019s QwQ-32B-Preview<\/a>, to generate the initial training data for Sky-T1, then \u201ccurated\u201d the data mixture and leveraged OpenAI\u2019s GPT-4o-mini<\/a> to refactor the data into a more workable format. Training the 32-billion-parameter Sky-T1 took about 19 hours using a rack of 8 Nvidia H100 GPUs. (Parameters roughly correspond to a model\u2019s problem-solving skills.)<\/p>\n According to the NovaSky team, Sky-T1 performs better than an early preview version of o1 on MATH500, a collection of \u201ccompetition-level\u201d math challenges. The model also beats the preview of o1 on a set of difficult problems from LiveCodeBench, a coding evaluation. <\/p>\n However, Sky-T1 falls short of the o1 preview on GPQA-Diamond, which contains physics, biology, and chemistry-related questions a PhD graduate would be expected to know.<\/p>\n Also important to note is that OpenAI\u2019s GA release of o1<\/a> is a stronger model than the preview version of o1, and that OpenAI is expected to release an even better-performing reasoning model, o3<\/a>, in the weeks ahead.<\/p>\n But the NovaSky team says that Sky-T1 only marks the start of their journey to develop open source models with advanced reasoning capabilities. <\/p>\n \u201cMoving forward, we will focus on developing more efficient models that maintain strong reasoning performance and exploring advanced techniques that further enhance the models\u2019 efficiency and accuracy at test time,\u201d the team wrote in the post. \u201cStay tuned as we make progress on these exciting initiatives.\u201d<\/p>\n<\/div>\n