Highlights:
- Researchers uncover the root cause of instability in RL fine-tuning of large language models.
- FP16 precision eliminates the mismatch between training and inference phases.
- The fix is simple, requiring minimal code changes and no architecture modifications.
- Results show improved stability, faster convergence, and stronger overall performance.
TLDR:
A new study led by Penghui Qi and colleagues demonstrates that using FP16 instead of BF16 resolves the long-standing training-inference mismatch in reinforcement learning fine-tuning of large language models, achieving more stable and efficient optimization with minimal implementation effort.
A research team led by Penghui Qi (https://arxiv.org/search/cs?searchtype=author&query=Qi,+P), alongside Zichen Liu (https://arxiv.org/search/cs?searchtype=author&query=Liu,+Z), Xiangxin Zhou (https://arxiv.org/search/cs?searchtype=author&query=Zhou,+X), Tianyu Pang (https://arxiv.org/search/cs?searchtype=author&query=Pang,+T), Chao Du (https://arxiv.org/search/cs?searchtype=author&query=Du,+C), Wee Sun Lee (https://arxiv.org/search/cs?searchtype=author&query=Lee,+W+S), and Min Lin (https://arxiv.org/search/cs?searchtype=author&query=Lin,+M), has unveiled a simple yet powerful solution to a persistent problem in large language model (LLM) training. Their paper, titled ‘Defeating the Training-Inference Mismatch via FP16’, identifies the numerical instability in reinforcement learning (RL) fine-tuning as a consequence of floating-point precision errors rather than algorithmic flaws or model discrepancies.
The training-inference mismatch has long been a major bottleneck in scaling and stabilizing RL fine-tuning of LLMs. While most frameworks use BF16 precision for its wide dynamic range, the researchers discovered that BF16 introduces excessive rounding errors that disrupt consistency between training and inference policies. These inconsistencies lead to unpredictable behaviors, degraded performance, and slower model convergence. The team’s experiments show that switching to FP16 precision — a 16-bit floating-point format with higher resolution — seamlessly resolves this issue.
One of the most remarkable aspects of this finding is its simplicity. According to the authors, implementing FP16 across training and inference requires just a few lines of code. The approach is fully supported by major deep learning frameworks and does not require any modification to model architecture, optimizer design, or loss function. The outcome is consistently more stable optimization, smoother convergence curves, and stronger performance across various tasks and algorithms. This discovery encourages the AI research community to re-examine the trade-offs between floating-point formats and their real-world performance implications in large-scale training.
The paper “Defeating the Training-Inference Mismatch via FP16” (arXiv:2510.26788) is likely to influence both academic and industrial machine learning practices, particularly those involving reinforcement learning fine-tuning. By highlighting the impact of numerical precision on training dynamics, Penghui Qi and collaborators call for a renewed focus on precision-aware AI optimization — paving the way for more robust and efficient model development.
Source:
Source:
arXiv:2510.26788 [cs.LG] — ‘Defeating the Training-Inference Mismatch via FP16’ by Penghui Qi et al. (https://doi.org/10.48550/arXiv.2510.26788)