ChainCatcher 消息，分布式 AI 实验室 Gradient 今日发布 Echo-2 分布式强化学习框架，旨在打破 AI 研究训练效率壁垒。通过在架构层实现 Learner 与 Actor 的彻底解耦，Echo-2 将 30B 模型的后训练成本从 4,500 美元骤降至 425 美元。在同等预算下，带来超过10倍的科研吞吐。
该框架利用存算分离技术进行异步训练 (Async RL)，将海量的采样算力卸载至不稳定显卡实例与基于 Parallax 的异构显卡。配合有界陈旧性、实例容错调度、与自研 Lattica 通讯协议等技术突破，在保证模型精度的同时大幅提升训练效率。伴随框架发布，Gradient 也即将推出 RLaaS 平台 Logits，推动 AI 研究从“资本堆砌”向“效率迭代”范式转移。Logits现已面向全球学生与研究人员开放预约。
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