Nanbeige4.2-3B: Unlocking Agentic Capabilities in a Compact Mode

2026-07-24

AI Summary

This work addresses the challenge of enabling small-scale models to achieve strong agentic capabilities under strict parameter constraints by proposing a compact, general-purpose agent model with only 3 billion non-embedding parameters. The model is trained from scratch on 28 trillion tokens of diverse real and synthetic agent trajectories and employs an innovative Looped Transformer architecture that reuses layer stacks to increase effective capacity without expanding parameter count. Enhanced through hybrid-mode RLHF, length-controlled reinforcement learning, and a dual reward mechanism balancing process and outcome, the model demonstrates significantly improved performance in multi-task reasoning, code generation, and tool use. It outperforms larger counterparts such as Qwen3.5-9B and Gemma4-12B across multiple agent benchmarks, excelling in mathematical, programming, scientific reasoning, and alignment tasks, thereby serving as an efficient and lightweight local personal assistant.

Abstract

We present Nanbeige4.2-3B, a compact general agentic model with 3B non-embedding parameters. It delivers strong performance across code-agent, office-agent, and complex tool-use tasks while maintaining highly competitive reasoning capabilities in mathematics, coding, and science. Nanbeige4.2-3B is pretrained from scratch on 28T tokens with a Looped Transformer that reuses the layer stack to increase capacity without adding parameters. For SFT data and trajectory construction, we expand the diversity of executable environments, task assets, and agentic scaffolds through real-world deployment and large-scale synthesis. Our RL pipeline applies mixed-mode RLHF over Think and Non-Think responses to improve overall model quality and reduce failure cases, length-controlled reasoning RL to balance accuracy and reasoning efficiency, and agentic RL with outcome and process rewards to stabilize long-horizon training. Extensive evaluations show that Nanbeige4.2-3B outperforms larger models, including Qwen3.5-9B and Gemma4-12B, across diverse agentic benchmarks while remaining competitive on reasoning and alignment tasks. Performance with OpenClaw further supports its use as a compact local personal assistant.

Problem

• agentic model
• compact model
• tool-use tasks
• reasoning capabilities
• personal assistant

Innovation

• Looped Transformer
• agentic scaffolds
• mixed-mode RLHF
• length-controlled reasoning RL
• compact agentic model