OpenBMB/Meshy v0.1.0

v0.1.0

Repository: OpenBMB/Meshy

Tag: v0.1.0

Published: 2026-09-09T06:07:09Z

Prerelease: no

Release notes: We are thrilled to release v0.1.0, the first release of Meshy, a ray-less asynchronous RL Engine for LLMs.

Highlights

• Ray-less, role-driven architecture

Meshy models inference, training, and rollout as independent services coordinated through a shared data plane.

• Native asynchronous RL support

The same service architecture supports on-policy, bounded off-policy, and fully asynchronous training workflows.

• Declarative and flexible topology

Define your training topology in a Python recipe. Each process derives its own placement locally under an SPMD paradigm, without requiring centralized service discovery.

• Unified data and control plane

TransferQueue transports tensors, synchronization signals, and GPU ownership tokens, eliminating the need for direct RPC coordination between services.

• Flexible GPU colocation

Services can be colocated or disaggregated across GPUs, with arbitrary service groups sharing resources through TransferQueue-managed ownership.

• Long-context RL training

Meshy supports context parallelism, dynamic batching, custom advantage shaping, and up to 128K-token contexts.

• Reproducible training recipes

This release includes recipes for GRPO on GSM8K and MATH, JustRL reproduction, and fully asynchronous RL training.

• Lightweight and debuggable runtime

Per-service logs, complete tracebacks, and queue-level visibility make distributed training runs easier to inspect and diagnose

Recipes

• GRPO + GSM8K for minimal baseline and smoke test.
• JustRL, including async and fully async variants, for reproducing JustRL and demonstrating Meshy's native asynchronous capabilities.
• GRPO + MATH for long context CP-enabled training on MiniCPM5-2B model.

Docker Image

docker pull ztonyzhao/meshy:0.1.0

Contributors

@zTonyZhao, @MayDomine