Tensor

Just read this new paper from Microsoft and i think this is gonna solve a lot of headache. Anyone who's tried RL-tuning agents inside real inference setups (like Claude Code or OpenClaw) knows the pain. Current open RL stacks just aren't built for stateful, multi-process harnesses, so you almost always end up with a nasty train-deploy mismatch. Their fix is ridiculously simple in hindsight: just completely decouple inference from training. They use a light proxy to log model calls during rollouts and throw each rollout into its own remote container using Kubernetes. That data just streams back into standard RL frameworks like veRL. No hacky workarounds. And the results are wild for the amount of data used: just a few hundred to a couple thousand tasks and OpenForge-GUI is pulling 37.7 on OSWorld-Verified and 72 on WebVoyager. It’s outperforming baselines of similar size and matching models way larger than it. Also pretty interesting to see their analysis on behavior: RL clearly turns the dial on stuff like self-verification and tool coverage, but agents are still pretty awful at actual error recovery when things go off the rails. Definitely give it a look if you're working on agent infrastructure. Read the full paper here: arxiv.org/pdf/2607.21557