MiroThinker 1.5 - The 30B That Outperforms 1T Models

Quick Overview

The MiroThinker 1.5 model, available in 30B and 235B parameter versions based on Qwen models, achieves state-of-the-art performance among open-source agents by emphasizing Interactive Scaling—the frequency and depth of tool interaction—as a third critical dimension alongside model size and context length, allowing it to outperform models up to 1T parameters in specific search agent benchmarks.

Key Points: MiroThinker 1.5 (30B and 235B variants) surpasses larger 1T parameter models on multiple search agent benchmarks, achieving a top score of 69.8 on BrowseComp with the 235B version. The core innovation is 'Interactive Scaling,' treating tool interaction frequency and depth as a third scalable dimension alongside model size and context length. MiroThinker 1.5 supports a 256K context window and handles up to 400 tool calls per task, representing a substantial improvement over previous open-source research agents. The training pipeline involves three stages: Supervised Fine-Tuning (SFT) on expert trajectories, Preference Optimization (DPO) using pairwise preferences, and Agentic Reinforcement Learning (GRPO) with environment interaction. The model is trained to behave like a 'scientist' that verifies information using a suite of tools (code execution, file management, web search) rather than relying solely on memorized knowledge. When running locally, the 30B model requires significant GPU RAM (85.2 GB shown for the A100-80GB), but the architecture is designed to allow for running smaller, quantized versions effectively. The agent's workflow involves an iterative loop of User Question -> Assistant Think/Tool Call -> User Tool Result -> Assistant Think/Tool Call, ensuring diligent verification and avoiding simple repetition loops.

Context: This video introduces MiroThinker 1.5, an open-source research agent developed by the MiroMind Team, detailed in a paper released around November 2025. The central theme is demonstrating how this model achieves superior performance compared to much larger models (like 1T parameter models) not through brute parameter dominance, but through a novel approach to tool-augmented reasoning called 'Interactive Scaling.' The demonstration walks through setting up the environment, showcasing the model's planning and tool-use capabilities, and presenting benchmark results that validate the effectiveness of this scaling strategy.