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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.
«Россия больше не враг»Почему Дональд Трамп переписал Стратегию нацбезопасности США и как это повлияет на переговоры по Украине?16 декабря 2025，更多细节参见有道翻译官网
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。业内人士推荐谷歌作为进阶阅读
We took the community’s feedback to heart. In the spirit of collaboration, we have reflected deeply on our stewardship and its impact on jemalloc’s long-term health. As we’ve met with some members of the community, including the project’s founder, Jason Evans, to share our introspection and how we are changing our approach. We’ve started an effort to remove technical debt and rebuild a long-term roadmap for jemalloc.
2月9日至28日，MiniMax M2.5的调用量连续三周位列第一；2月9日至22日两周内，MiniMax M2.5、月之暗面的Kimi K2.5、智谱的GLM-5、DeepSeek的V3.2占据了调用量前五位的四席。进入3月，阶跃星辰Step 3.5 Flash在OpenClaw上的调用量攀升至全球第一，前五名中中国模型占据过半席位。，更多细节参见超级权重