Author

Zhuo-Ran Jin

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Preprint Aug 2026

SFT Conflicts, RL Coexists: A Theoretical and Empirical Analysis of Multi-Task Learning for LLMs

Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL) exhibit fundamentally different behaviors in enhancing multi-task reasoning for large language models (LLMs). Our preliminary experiments revealed a phenomenon: SFT suffers from severe task conflicts under multi-stage training, whereas RL enables stable coexi...

Kejian Zhu, Zhuo-Ran Jin, Shangqing Tu et al. · 0 citations

#artificial intelligence Preprint Sep 2026

Environments as Scaffold: Enriching Feedback to Bootstrap Self-Evolving Agents in Long-Horizon Tasks

Large Language Models demonstrate remarkable proficiency in static reasoning, yet training them as autonomous agents through Reinforcement Learning (RL) for long-horizon tasks is often hindered by severe reward sparsity. While conventional \textit{agent-side warming} up via supervised fine-tuning (SFT) can alleviate th...

Hongbang Yuan, Zhuo-Ran Jin, Yi-Xin Cao · 0 citations

#natural language process... Preprint Aug 2026

SwarmBench: Can Large Language Models Act as Agent Swarm Orchestrators?

SwarmBench is proposed, a benchmark that evaluates model performance from multiple perspectives, including accuracy, efficiency, cost, and process quality, and SwarmExp is proposed, a simple yet effective method based on experience extraction and experience replay, which consistently improves the orchestration performa...

Jin Gao, Zhuoran Jin, Tianyi Men et al. · 0 citations