FP16 precision breakthrough stabilizes reinforcement learning for language models
A recent study has made a significant breakthrough in reinforcement learning for large language models. The research, conducted across two independent frameworks, VeRL and Oat, demonstrates consistent performance improvements across various tasks and algorithms.
The key finding is that switching to the FP16 format eliminates rounding errors, leading to more stable and faster learning. This change requires minimal code adjustments and no architectural modifications, making it a simple and robust approach. The study used diverse settings, including algorithms like GRPO, GSPO, TIS, MIS, and PG, and model families like R1D, Qwen, and OctoThinker, proving the universality of this method.
The research reveals that the instability in refining large language models using reinforcement learning is due to rounding errors introduced by the BF16 format. FP16 precision closes the deployment gap, ensuring final model parameters are optimized for real-world applications. It also addresses the training-inference mismatch, a common challenge in model deployment.
The study offers a simpler and more robust approach to reinforcement learning fine-tuning for large language models. It shows that complex algorithmic workarounds are not necessary with FP16 precision. This finding has the potential to greatly simplify and improve the process of refining large language models using reinforcement learning.
