Reef Releases Weight Release Gate Recipe for OpenClaw-RL

Reef has released a weight-evolution recipe for OpenClaw-RL, presenting a workflow where scored weight updates must pass test gates before being reflected in the live serving state.

Technical Configuration and Performance

This recipe uses Qwen3-4B as the policy and process reward model, and Qwen3-32B as the student model, running on 7 GPUs. Training proceeds by treating 72 problems from GSM8K as 72 tasks, and according to the project report, the criteria for three consecutive passes were met in the 14th session. The published learning curve includes the first 36 sessions.

Usage Guide and Limitations

These figures should be considered a bounded reproduction target rather than a benchmark representative of all OpenClaw workloads. Users should reproduce the recipe with OpenClaw tasks that have objective verifiers, confirm that intentionally incorrect weight candidates do not alter the serving state, and then assess the potential for transfer to real-world task optimization.