DAPO makes large-scale LLM reinforcement learning fully reproducible
Researchers can now reproduce a large-scale LLM reinforcement-learning system with its algorithm, code infrastructure, dataset, and model weights in one open repository. DAPO trains Qwen2.5-32B to 50 points on AIME 2024, and the project says it reaches that result with half the training steps of DeepSeek-R1-Zero-Qwen-32B. Its core method combines decoupled clipping with dynamic sampling to keep training stable while the model explores harder reasoning tasks.