Slow-Fast Policy Optimization: Reposition-Before-Update for LLM
  Reasoning
Authors: name: Ziyan Wang, name: Zheng Wang, name: Jie Fu, name: Xingwei Qu, name: Qi Cheng, name: Shengpu Tang, name: Minjia Zhang, name: Xiaoming Huo

Reinforcement learning (RL) has become central to enhancing reasoning in
large language models (LLMs). Yet on-policy algorithms such as Group Relative
Policy Optimization (GRPO) often suffer in early training: noisy gradients from
low-quality rollouts lead to unstable updates and inefficient exploration. We
introduce Slow-Fast Policy Optimization (SFPO), a simple yet efficient
framework to address these limitations via decomposing each step into three
stages: a short fast trajectory of inner steps on the same batch, a reposition
mechanism to control off-policy drift, and a final slow correction. This
reposition-before-update design preserves the objective and rollout process
unchanged, making SFPO plug-compatible with existing policy-gradient pipelines.
Extensive experiments demonstrate that SFPO consistently improves stability,
reduces rollouts, and accelerates convergence of reasoning RL training.
Specifically, it outperforms GRPO by up to 2.80 points in average on math
reasoning benchmarks. It also achieves up to 4.93 fewer rollouts
and a 4.19 reduction in wall-clock time to match GRPO's best
accuracy.