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.
10K preference pairs in math and coding domains, generated by Sky-T1-32B-Preview.
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.
We use Llama-Factory for training. On 8xH100, the SimPO training takes ~2.5 hours with DeepSpeed Zero-3 Offload.
We would like to thanks the compute resources from
Lambda Lab and
AnyScale.
Please considering citing our blog post if you found it useful for your research. Thank you!
1@misc{reduce_overthinking_2025,
2 author = {NovaSky Team},
3 title = {Think Less, Achieve More: Cut Reasoning Costs by 50% Without Sacrificing Accuracy},
4 howpublished = {https://novasky-ai.github.io/posts/reduce-overthinking},
5 note = {Accessed: 2025-01-23},
6 year = {2025}
7}