Overview
Osmosis-MCP-4B is based on the Qwen3-4B model, fine-tuned with reinforcement learning to excel at multi step MCP-style tool usage.
We trained Osmosis-MCP-4B using a custom curriculum of multi-turn, tool-reliant prompts that mimic real-world use cases — for example:
"Given the weather in San Francisco, what are the top hiking locations?"
In addition, we provide a list of deterministic MCP like functions and mock server side behavior for the model to call and use.
This requires the model to reason through multiple tool invocations (e.g., weather → location ranker), and choose tools over intuition when applicable.
Training Approach
Our training pipeline leverages:
- Dr. GRPO for stable and sample-efficient reinforcement learning.
- Synthetic multi-step MCP interactions with strong tool chaining behavior, generated using our internal data engine.
- SGLang + VeRL for efficient multi-turn rollout environments, built on top of Qwen3-4B for its function-calling capabilities.
Through this training methodology, we observed a notable behavioral shift: the model prefers invoking tools when appropriate, instead of relying solely on pre-trained intuition — a key milestone for MCP-native agents.
Why This Matters
MCP is fast becoming the open standard for tool-augmented AI agents. However:
- Most top-performing models (e.g., Claude 3.7 Sonnet, Gemini 2.5 Pro) are closed.
- Tool sprawl across clients and servers creates complexity.
- Open models often lack the training to effectively use tools at all.
