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Open nowPosted 43 days ago

大模型后训练与智能体学习科研实习生

sii-czxy/campus170 open roles

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Your applicationOpen now大模型后训练与智能体学习科研实习生sii-czxy/campus

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Share of postings closed within
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  6. 57.8%60 days
This job: posted 43 days ago

The posting

1. 调研与复现大模型后训练方法：阅读并整理大语言模型后训练、监督微调（SFT）、强化学习、知识蒸馏和 Agent Learning 等方向前沿论文，复现代表性开源方法。 2. 构建 Agent 交互数据体系：整理智能体在小游戏开发过程中的成功轨迹、失败—反馈—修复轨迹、工具调用记录及评测反馈数据，构建面向 Agent 学习的数据集。 3. 开展模型训练与优化实验：参与轨迹微调、监督微调（SFT）、奖励建模、长轨迹信用分配以及 on-policy distillation 等训练方法研究，探索提升 Agent Coding 能力的有效策略。 4. 评估不同训练方法效果：设计实验方案，对比不同训练策略在任务完成率、代码生成质量、错误修复能力、运行稳定性和泛化能力方面的表现，并完成消融实验分析。 5. 探索 Agent 自我提升机制：研究如何利用强模型生成数据、环境反馈和多智能体协作机制提升国产基座模型的 Agentic Coding 能力，并参与阶段性成果总结和论文撰写。

1. 招募对象：本科生、硕士研究生。 2. 修读过机器学习、深度学习、概率统计等相关课程，了解 Transformer、大语言模型基本原理。 3. 具备 Python / PyTorch 编程能力，能够完成模型训练、实验运行、数据处理和结果分析。 4. 有 Hugging Face Transformers、分布式训练、LLM SFT/RL、强化学习、多模态模型或 Agent 相关实践经验者优先。 5. 有论文复现、科研项目、算法竞赛或开源项目经历者优先，不要求已有论文发表。 6. 能够阅读英文论文和技术资料，持续跟进大模型后训练、Agent Learning 等前沿方向。 7. 具备较强的问题分析和实验设计能力，能够稳定投入科研工作。 8. 对大模型智能体、自动化软件开发和多模态 Agent 系统具有浓厚兴趣。 9. 实习安排：线上线下结合；周期3–6个月；每周投入10–20小时。

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