In a significant advancement for large language model (LLM) training efficiency, Kwai AI has unveiled its novel SRPO framework, which claims to reduce the computational overhead of reinforcement learning (RL) post-training by up to 90%—while maintaining performance levels comparable to DeepSeek-R1, a leading open-source model in math and code tasks.
Revolutionizing RL Training with SRPO
The framework, named SRPO (Sample Recycling Policy Optimization), introduces a two-stage RL approach that leverages history resampling to overcome the inefficiencies inherent in traditional GRPO (Generalized Policy Optimization) methods. By reusing previously generated samples, SRPO minimizes redundant computations, a key bottleneck in current RL training pipelines. This approach not only cuts down on training time but also significantly reduces the computational resources required, making large-scale LLM fine-tuning more accessible and sustainable.
Efficiency Gains and Performance
According to Kwai AI’s research, SRPO achieves a 10x improvement in training efficiency compared to GRPO, without sacrificing accuracy in complex reasoning tasks. The framework's ability to maintain performance while drastically reducing steps is particularly promising for companies aiming to scale their AI models without incurring massive infrastructure costs. The two-stage structure ensures that the policy updates are both stable and effective, mitigating the risk of performance degradation often seen in more aggressive optimization techniques.
Implications for the AI Industry
This development marks a pivotal moment in the evolution of LLM training methodologies. As AI models grow in complexity and size, the demand for efficient training frameworks becomes increasingly critical. SRPO’s success suggests a new direction for the industry—one that prioritizes sustainability and scalability without compromising on performance. With further refinement and adoption, SRPO could become a standard tool in the AI training toolkit, reshaping how companies approach reinforcement learning for large language models.
