Klear-AgentForge: Forging Agentic Intelligence through Posttraining Scaling

• URL: http://arxiv.org/abs/2511.05951v1
• Date: Sat, 08 Nov 2025 09:47:27 GMT
• Title: Klear-AgentForge: Forging Agentic Intelligence through Posttraining Scaling
• Authors: Qi Wang, Hongzhi Zhang, Jia Fu, Kai Fu, Yahui Liu, Tinghai Zhang, Chenxi Sun, Gangwei Jiang, Jingyi Tang, Xingguang Ji, Yang Yue, Jingyuan Zhang, Fuzheng Zhang, Kun Gai, Guorui Zhou,
• Abstract summary: We present a comprehensive and fully open-source pipeline for training a high-performance agentic model, named Klear-Qwen3-AgentForge.<n>We design effective supervised fine-tuning (SFT) with synthetic data followed by multi-turn reinforcement learning (RL) to unlock the potential for multiple diverse agentic tasks.
• Score: 46.593200463657645
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Despite the proliferation of powerful agentic models, the lack of critical post-training details hinders the development of strong counterparts in the open-source community. In this study, we present a comprehensive and fully open-source pipeline for training a high-performance agentic model for interacting with external tools and environments, named Klear-Qwen3-AgentForge, starting from the Qwen3-8B base model. We design effective supervised fine-tuning (SFT) with synthetic data followed by multi-turn reinforcement learning (RL) to unlock the potential for multiple diverse agentic tasks. We perform exclusive experiments on various agentic benchmarks in both tool use and coding domains. Klear-Qwen3-AgentForge-8B achieves state-of-the-art performance among LLMs of similar size and remains competitive with significantly larger models.

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