News

• [2025/02/11] 🎉 We released Sky-T1-7B (model) and Sky-T1-mini (model) to demonstrate the potential of RL in further enhancing model's capability beyond distillation.
• [2025/01/23] ⚡️ We released Sky-T1-32B-Flash (model, data) to tackle overthinking and reduce reasoning sequence lengths while maintaining accuracy.
• [2025/01/19] 🎉 Chat demo for Sky-T1-32B-Preview is alive! Please check it out!
• [2025/01/10] 🎉 We have released our Sky-T1-32B-Preview model and data through HuggingFace!

Links

• 📜 Sky-T1-7B and Sky-T1-mini Blog Post
• 📜 Sky-T1-32B-Flash Blog Post
• 📜 Sky-T1-32B-Preview model Blog Post
• 🤗 Sky-T1-32B-Preview model

Getting Started

We open source the code and scripts we used for data curation, training, and evaluation for Sky-T1-32B-Preview, you can find more details in each directory.

• recipes: Recipes - data curation steps and training strategies - for building our models Sky-T1-32B-Flash, Sky-T1-32B-Preview and Sky-T1-7B series.
• skythought/evals: Our data generation and evaluation library.
• skythought/train: Training scripts for Sky-T1. We use Llama-Factory to perform training.
• skythought/skythought-rl: RL training code for Sky-T1-7B and Sky-T1-mini.

Evaluation

Usage

First, clone the repository and install the package

git clone https://github.com/NovaSky-AI/SkyThought.git
cd SkyThought
# installs shown for uv
uv venv python==3.10
source .venv/bin/activate
uv pip install -e .

Running evaluation is as simple as:

skythought evaluate --model NovaSky-AI/Sky-T1-32B-Preview --task aime

We support a wide variety of datasets in mathematics, science and coding:

• AIME'24
• MATH500
• GPQADiamond
• MMLU
• ARC-Challenge
• OlympiadBench
• AMC'23
• TACO
• APPS
• LiveCodeBench
• MMLU Pro
• MinervaMath
• GSM8K
• AIME'25

For more details, please refer to our evaluation guide and the README.

Evaluation results

Following, we show our evaluation results for the Sky-T1-32B-Preview model across math, coding, and science benchmarks.

Results on non-reasoning benchmarks

We also evaluate on non-reasoning benchmarks (these are benchmarks for instruction-following, QA, etc) to test whether the model has traded-off capability in other domains for better performance in reasoning-related benchmarks.

For more details, refer here.

Fully Open-source: Driving Progress Together

We believe that open-source collaboration drives progress, and with Sky-T1-32B-Preview, we are fully committed to empowering the community. We open-source all details (i.e., data, codes, model weights) to enable the community to replicate and improve on our results easily:

Citation

The code in this repository is mostly described in the post below. Please consider citing this work if you find the repository helpful.

@misc{sky_t1_2025,
  author       = {NovaSky Team},
  title        = {Sky-T1: Train your own O1 preview model within $450},
  howpublished = {https://novasky-ai.github.io/posts/sky-t1},
  note         = {Accessed: 2025-01-09},
  year         = {2025}
}

Acknowledgement

This work is done at Berkeley Sky Computing Lab, with the amazing compute support from Lambda Labs, Anyscale, and Databricks. We would like to express our gratitude for the valuable academic feedback and support from the Still-2 Team, and Junyang Lin from the Qwen Team.