Kwai AI's SRPO Boosts LLM RL Efficiency by 10x
Kwai AI's SRPO framework reduces LLM reinforcement learning post-training steps by 90% while matching DeepSeek-R1 performance in math and code tasks.
Why it matters
SRPO's 10x efficiency improvement could have a major impact on accelerating the development and deployment of advanced LLMs.
Key Points
- 1Kwai AI's SRPO framework improves on GRPO with a two-stage RL approach and history resampling
- 2SRPO slashes LLM RL post-training steps by 90% compared to previous methods
- 3SRPO matches the performance of DeepSeek-R1 on math and coding benchmarks
Details
Kwai AI has developed a new reinforcement learning (RL) framework called SRPO that significantly boosts the efficiency of large language model (LLM) training. SRPO uses a two-stage RL approach with history resampling to overcome the limitations of the standard GRPO (Generalized Proximal Policy Optimization) method. This allows SRPO to reduce the number of post-training RL steps by 90% while still matching the performance of the DeepSeek-R1 model on math and coding benchmarks. The key innovation in SRPO is its ability to effectively leverage past experiences during the RL process, leading to much faster convergence compared to traditional RL techniques.
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