InfiR2: A Comprehensive FP8 Training Recipe for Reasoning-Enhanced Language Models

Main Contributions

• End-to-End FP8 Training Recipe: InfiR2 is the first open-source, end-to-end FP8 training recipe that unifies continual pre-training and supervised fine-tuning in one workflow, offering a practical alternative to traditional BF16 training. It resolves the longstanding difficulties that prevented FP8 from being used in full model training pipelines.
• Hybrid-Granularity Quantization: InfiR2 implements finer-grained FP8 quantization for computationally intensive operators, as shown in Figure 1: -For Linear/GEMM operations, it applies block-wise quantization to weights and token-wise quantization to activations. This balances accuracy with computational speed to fully leverage the hardware value of Tensor Cores. -For critical components like Master Weights, Optimizer States, and Gradient Accumulation, it employs a high-precision strategy for direct optimization and updates, effectively providing a “safety belt” for the most crucial parts of model training.
• Stable and Reproducible Performance: We demonstrate for the first time that FP8 training can match full-precision training in quality across critical reasoning benchmarks. InfiR2’s FP8-trained models achieve accuracy on par with BF16 on tasks like AIME, GPQA, and LiveCodeBench (often within 1-2% or less). Notably, for smaller models (e.g. ~7B parameters), FP8 training even slightly outperforms BF16 on some benchmarks (acting as a form of regularization), confirming that FP8 is not only computationally lighter but also rock-solid in training convergence.
• Community Release & Impact: Based on this complete FP8 training workflow (applying CPT and SFT to the Qwen-2.5 model series), we have developed two high-performing models: InfiR2-1.5B-FP8 and InfiR2-7B-FP8. We are open-sourcing the model checkpoints and the complete training code repository to the community.

Pretraining performance comparison between FP8 and BF16.

Experiments

• Pretraining with FP8 is lossless. InfiR2-1.5B-FP8 shows almost no performance loss on AIME25, AIME24, GPQA, and LiveCodeBench compared to the BF16 baseline, as detailed in the results below.

• SFT with FP8 is lossless. After fine-tuning, Qwen2.5-Math-1.5B and 7B also show no degradation compared to the BF16 baseline, and even achieve a 1-2 point improvement on the AIME math competition dataset, as detailed in the results below.

• Memory optimization and Speed up. Memory Optimization & Computation Acceleration: Compared to the widely used BF16, FP8 delivers: -Up to 22% increase in end-to-end training speed. -Up to 14% savings in peak memory usage. -Up to 19% increase in end-to-end throughput.

Model Size = 1.5B

Model Size = 7B

Citation Information

If you find this work useful, citations to the following papers are welcome:

@misc{wang2025infir2comprehensivefp8training,
      title={InfiR2: A Comprehensive FP8 Training Recipe for Reasoning-Enhanced Language Models},
      author={Wenjun Wang and Shuo Cai and Congkai Xie and Mingfa Feng and Yiming Zhang and Zhen Li and Kejing Yang and Ming Li and Jiannong Cao and Hongxia Yang},
      year={2025},
      eprint={2509.22536},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2509.22536},
}

Acknowledgements

We would like to express our gratitude for the following open-source projects: Slime, LLaMA-Factory, and Qwen2.5