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---
library_name: transformers
datasets:
- BAAI/TACO
- tasksource/PRM800K
language:
- en
base_model:
- Qwen/Qwen2.5-32B-Instruct
- NovaSky-AI/Sky-T1-32B-Preview
license: apache-2.0
---
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is a 32B reasoning model preference optimized on top of Sky-T1-32B-Preview to significantly reduce generation lengths while maintaining accuracy. The performance is on par with o1-preview model in both math and coding, while reducing generation lengths by up to 57% relative to Sky-T1-32B-Preview.
Please see our [blog post](https://novasky-ai.github.io/posts/reduce-overthinking/) for more details.
- **Developed by:** NovaSky Team from Sky Computing Lab at UC Berkeley.
## Training Details
### Training Data
10K preference pairs in math and coding domains, generated by Sky-T1-32B-Preview.
### Training Procedure
We perform Simple Policy Optimization (SimPO) with a batch size of 96, learning rate of 5e-7, gamma of 0.3, and beta of 2.0.
#### Speeds
We use Llama-Factory for training. On 8xH100, the SimPO training takes ~2.5 hours with DeepSpeed Zero-3 Offload.
## Evaluation
| | | Sky-T1-32B-Preview | Sky-T1-32B-Flash | Qwen2.5-32B-Instruct | QwQ-32B- Base | DeepSeek-R1-Distill-Qwen-32B |
|--------------|---------|:------------------:|:----------------:|:--------------------:|:-------------:|:----------------------------:|
| Math500 | Acc | 88.6 | 88.6 | 76.2 | 89.2 | 90.8 |
| | Avg Len | 2124 | 1417 (-33%) | 522 | 2089 | 2010 |
| AIME24 | Acc | 43.3 | 43.3 | 16.7 | 50 | 66.7 |
| | Avg Len | 6881 | 4365 (-37%) | 970 | 7379 | 9173 |
| LCB Easy | Acc | 87.4 | 89 | 84.6 | 90.7 | 91.2 |
| | Avg Len | 3415 | 2265 (-34%) | 414 | 3255 | 2775 |
| LCB Medium | Acc | 56.8 | 56.3 | 40.8 | 56.3 | 76.7 |
| | Avg Len | 8263 | 4389 (-47%) | 535 | 6742 | 6324 |
| LCB Hard | Acc | 17.9 | 17.9 | 9.8 | 17.1 | 38.2 |
| | Avg Len | 14564 | 6199 (-57%) | 618 | 10450 | 10448 |
| MMLU | Acc | 82.4 | 81.7 | 80.1 | 85.2 | 82.1 |
| | Avg Len | 1087 | 799 (-17%) | 312 | 1041 | 774 |
| GPQA Diamond | Acc | 56.8 | 56.6 | 45.5 | 52.5 | 62.6 |
| | Avg Len | 3503 | 2148 (-39%) | 600 | 3302 | 5108 |
## Acknowledgement
We would like to thanks the compute resources from [Lambda Lab](https://lambdalabs.com/service/gpu-cloud?srsltid=AfmBOop5FnmEFTkavVtdZDsLWvHWNg6peXtat-OXJ9MW5GMNsk756PE5) and [AnyScale](https://www.anyscale.com/).
## License
Apache-2.0
## Citation
Please considering citing our blog post if you found it useful for your research. Thank you!
```bibtex
@misc{reduce_overthinking_2025,
author = {NovaSky Team},
title = {Think Less, Achieve More: Cut Reasoning Costs by 50% Without Sacrificing Accuracy},
howpublished = {https://novasky-ai.github.io/posts/reduce-overthinking},
note = {Accessed: 2025-01-23},
year = {2025}
}
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