NovaSky-AI/Sky-T1-32B-Flash

Model Details

Model Description

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 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 and AnyScale.

License

Apache-2.0

Citation

Please considering citing our blog post if you found it useful for your research. Thank you!

@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}
}