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The RL system is implemented with an asynchronous GRPO architecture that decouples generation, reward computation, and policy updates, enabling efficient large-scale training while maintaining high GPU utilization. Trajectory staleness is controlled by limiting the age of sampled trajectories relative to policy updates, balancing throughput with training stability. The system omits KL-divergence regularization against a reference model, avoiding the optimization conflict between reward maximization and policy anchoring. Policy optimization instead uses a custom group-relative objective inspired by CISPO, which improves stability over standard clipped surrogate methods. Reward shaping further encourages structured reasoning, concise responses, and correct tool usage, producing a stable RL pipeline suitable for large-scale MoE training with consistent learning and no evidence of reward collapse.,详情可参考viber
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3月10日消息,独家获悉,荣耀CEO李健表示,人形机器人是荣耀阿尔法战略的一部分,使用场景将聚焦在消费市场,重点关注购物协助、工作检查、陪伴三大消费级场景。据悉,荣耀是全球第一家正式进军消费级人形机器人市场的手机厂商,首款人形机器人Robot已在MWC 2026上正式亮相。李健透露,在接下来的市场活动中会看到更多荣耀机器人的表现。