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This paper investigates training recipes for few-step distillation of visual generative models, using Qwen-Image-2.0 as a case study. It reveals non-obvious behaviors and proposes Qwen-Image-Flash.
MiniCPM5-1B is a 1B parameter model from OpenBMB that achieves impressive scores on AIME 2025 and τ2-Bench Telecom, outperforming larger models. It features both fast and reasoning modes from a single checkpoint, enabled by a three-stage post-training process including supervised fine-tuning, reinforcement learning, and on-policy distillation.