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Gradient Releases Echo-2 RL Framework, Boosting AI Research Efficiency by Over 10 Times

Gradient has released the Echo-2 distributed reinforcement learning framework (arxiv.org/pdf/2602.02192), designed to overcome efficiency barriers in AI research training. By decoupling Learners and Actors at the architectural level, Echo-2 reduces the post-training expense of a 30B model from $4,500 to just $425. Under the same budget, it delivers more than 10x improvement in research throughput. The framework uses compute-storage separation and asynchronous training (Async RL) to offload large-scale sampling to unreliable and heterogeneous GPU instances. It incorporates bounded staleness, fault-tolerant scheduling, and a custom Lattica communication protocol to maintain model accuracy while significantly boosting efficiency. Alongside the framework, Gradient is launching Logits, an RLaaS platform, to shift AI research paradigm from "capital-intensive" to "efficiency-driven". Logits is now open for global students and researchers for预约 (logits.dev).

marsbit02/12 16:39