README.md

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Advancing Language Model Reasoning through Reinforcement Learning and Inference Scaling

📃Paper • 🤗Datasets • 🤗Model (coming soon)

Introduction

We present T1 with strong reasoning ability and superior performance on challenging mathematical reasoning benchmarks. T1 is trained by scaling RL by encouraging exploration and understand inference scaling. We first initialize the LLM using synthesized chain-of-thought data that integrates trial-and-error and self-verification. T1 with open LLMs as its base exhibits inference scaling behavior and achieves superior performance on challenging math reasoning benchmarks.

• [2025/01/22] We have released the paper and SFT data. Model weights and RL training data will be released soon.

Figure 1: Training scaling and inference scaling of T1 on the AIME2024 dataset

Results

Our approach achieves competitive performance across challenging mathematical reasoning benchmarks:

Model	MATH500	AIME	Omni-MATH-500	GPQA
GPT-4o	76.6	9.3	26.8	53.6
Claude-3.5-sonnet	78.3	16.0	-	65.0
Llama-3.3-70B-Instruct	73.9	24.2	27.9	50.5
Qwen2.5-Math-7B-Instruct	82.7	16.7	29.7	36.9
o1-preview	85.5	44.6	-	72.3
QwQ-32B-preview	90.6	50.0	46.6	58.2
T1-SFT (GLM-4-9B)	60.2	4.1	20.0	37.2
T1 (GLM-4-9B)	65.8	9.2	24.4	38.1
T1-SFT (Qwen2.5-14B)	77.2	10.3	28.5	42.3
T1 (Qwen2.5-14B)	87.4	30.5	38.6	48.3
T1-SFT (Qwen2.5-32B)	83.4	24.9	34.6	49.5
T1 (Qwen2.5-32B)	92.4	50.6	49.6	56.1

📚 Citation

@article{hou2025advancing,
      title={Advancing Language Model Reasoning through Reinforcement Learning and Inference Scaling}, 
      author={Zhenyu Hou and Xin Lv and Rui Lu and Jiajie Zhang and Yujiang Li and Zijun Yao and Juanzi Li and Jie Tang and Yuxiao Dong},
      year={2025},
      journal={arXiv preprint arXiv:2501.11651},
}