Nanbeige4.2-3B

1. Introduction Nanbeige4.2-3B is a compact agentic model built on Nanbeige4.2-3B-Base, designed to combine strong agentic behavior with broad reasoning and alignment capabilities. Its Looped Transformer architecture reuses the transformer layers to increase model capacity without adding parameters. With only 3B non-embedding parameters, the model delivers solid performance on general-agent and code-agent tasks. During supervised fine-tuning (SFT), we expand the diversity of training environments through real-world environment integrations and large-scale environment synthesis. We further diversify task types, task assets, and the agentic scaffolds used for each task. To ensure training data quality, we apply filtering at both the trajectory and turn levels, combining test-case-based validation with rubric-based assessment. During reinforcement learning (RL), we combine outcome and process rewards to improve training stability for the compact model. Key strengths include: **Solid Agentic Behavior at the 3B Scale**: Across complex tool-use, office-agent, and code-agent benchmarks, Nanbeige4.2-3B outperforms larger models such as Qwen3.5-9B and Gemma4-12B. **Strong Reasoning Capabilities**: Nanbeige4.2-3B leads open-source models of comparable size across mathematical, coding, and scientific reasoning tasks, continuing the strong reasoning performance of Nanbeige4.1-3B. **Local Personal Assistant**: When integrated with an agentic scaffold designed for personal workflows (e.g., OpenClaw), Nanbeige4.2-3B can support extended tasks spanning daily assistance, office work, and deep research. The accompanying `modeling_nanbeige.py` also includes our latest architectural improvements, including **LoopSplit**, **mHC with depth attention**, and **concatenated n-gram embeddings**. These features have been incorporated into Nanbeige4.5, whose training is underway for release later in 2026. 2. Model Performance General and Agentic Capabilities We compare Nanbeige4.2-3B with Qwen