ReasonLite

**ReasonLite is an ultra-lightweight math reasoning model.** With only 0.6B parameters, it leverages **high-quality data distillation** to achieve performance comparable to models over 10× its size, such as Qwen3-8B, **reaching 75.2 on AIME24 and extending the scaling law of small models.** * 🔥 **Best-performing 0.6B math reasoning model** * 🔓 Fully open-source — weights, scripts, datasets, synthesis pipeline * ⚙️ Distilled in two stages to balance **efficiency** and **high performance**, using **6.1M** high-quality samples.

ReasonLite

--- # 🚀 Model The model is trained in **two progressive distillation stages**. First, short-CoT data is used to distill **Qwen3-0.6B** into **ReasonLite-0.6B-Turbo**, improving **AIME24 accuracy from 11.0 → 57.1**. Then, long-CoT data is used to obtain **ReasonLite-0.6B**, further boosting accuracy to **75.2**. | Model | Description | AIME24 | Link | | ------------------------- | ----------------------------------------------| ------ | ---- | | **amd/ReasonLite-0.6B-Turbo** | Short CoT balancing performance and efficiency | 57.1 | [🤗 HuggingFace](https://huggingface.co/amd/ReasonLite-0.6B-Turbo) | | **amd/ReasonLite-0.6B** | Long CoT for high performance | 75.2 | [🤗 HuggingFace](https://huggingface.co/amd/ReasonLite-0.6B) | --- # 📊 Evaluation Results **Metrics** * **avg@16** — average accuracy from 16 sampled answers * **pass@8** — probability at least one correct answer appears among 8 samples | Model | Parameters | AMC23 avg@16 | AMC23 pass@8 | AIME25 avg@16 | AIME25 pass@8 | AIME24 avg@16 | AIME24 pass@8 | |---------------------------|------------|-------------|-------------|---------------|---------------|---------------|---------------| | Qwen2.5-14B | 14B | 58.3 | 82.3 | 12.3 | 32.3 | 12.7 | 32.4 | | Deepseek-qwen-14B | 14B | 93.9 | 98.7 | 50.2 | 71.0 | 65.0 | 83.0 | | Qwen3-0.6B | 0.6B | 52.7 | 85.0 | 16.0 | 33.0 | 11.0 | 31.5 | | Qwen3-1.7B | 1.7B | 83.4 | 96.3 | 36.0 | 55.1 | 47.3 | 73.9 | | Qwen3-4B | 4B | 96.1 | 100 | 63.5 | 85.4 | 72.7 | 85.1 | | Qwen3-8B | 8B | 94.8 | 100 | 68.3 | 84.2 | 74.6 | 85.0 | | Qwen3-14B | 14B | 98.6 | 98.7 | 71.5 | 84.1 | 78.3 | 88.4 | | DeepscaleR-1.5B | 1.5B | 83.8 | 95.0 | 29.0 | 48.9 | 40.4 | 69.0 | | POLARIS-1.7B-Preview | 1.7B | 92.2 | 97.4 | 52.3 | 80.2 | 65.0 | 76.7 | | OpenMath-Nemotron-1.5B | 1.5B | 88.8 | 96.7 | 39.8 | 65.8 | 61.5 | 81.3 | | ReasonLite-0.6B-Turbo | 0.6B | 81.6 | 99.3 | 42.7 | 69.2 | 57.1 | 79.6 | | **ReasonLite-0.6B** | **0.6B** | **95.2** | **100** | **62.9** | **84.1** | **75.2** | **90.2** | --- # 📚 Dataset We collected 343K math problems from Polaris and OpenMathReasoning. Using GPT-OSS as the teacher, we generated 9.1M raw answers under medium and high reasoning modes. We then produced pseudo-labels via majority voting, and finally retained 6.1M samples. | Dataset | Description | Size | Link | | ---------------------- | ------ |---- | ---- | | **amd/ReasonLite-Dataset** | Short CoT | 4.3M | [🤗 HuggingFace](https://huggingface.co/datasets/amd/ReasonLite-Dataset/viewer/default/medium) | | **amd/ReasonLite-Dataset** | Long Cot | 1.8M | [🤗 HuggingFace](https://huggingface.co/datasets/amd/ReasonLite-Dataset/viewer/default/high) | --- # 📌 Citation ```bibtex @misc{reasonlite2025, title = {ReasonLite: An Ultra-Lightweight 0.6B Reasoning Model}, author = {An, Zihao and Chen, Chushi and Liu, Ziqiong and Li, Dong and Barsoum, Emad}, year = {2025}, url = {https://github.com/AMD-AGI/ReasonLite}, note = {Open-source project} } ```