AI BriefingHarborRL Adds Elastic Scaling and SSH-Free Deployment for Agentic RL
·2026.11.14 14:42
Key point
The November 2026 update introduces elastic gateway scaling, HTTP-based worker task pulling, and a shared Slime-based training pipeline for mathematical reasoning.
Details
- HarborRL, an open-source framework for agentic reinforcement learning, released a November 2026 update focused on simplifying distributed training and deployment.
- New elastic gateway scaling features include static routing, automatic multi-machine scaling, and automatic retirement of idle resources to improve utilization.
- A new Hub deployment mode allows worker nodes to pull tasks over HTTP, enabling distributed execution without cross-machine SSH access, even in firewall-restricted environments.
- Multiple RL algorithms are now integrated into a shared Slime-based training pipeline, validated on mathematical reasoning tasks to reduce configuration engineering effort.
- The project is available on GitHub under the MIT license.
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