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Open nowPosted 33 days ago

【校招】大模型智能体强化学习青年研究员/算法工程师-大模型中心

aicarrier/522799209 open roles

Where
Shanghai, Chinese Mainland
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Your applicationOpen now【校招】大模型智能体强化学习青年研究员/算法工程师-大模型中心aicarrier/522799 · Shanghai, Chinese Mainland

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The clock on this job

Early applications get read.

12.3% of postings close within 7 days. Measured by our own scanner across the market.

Share of postings closed within
  1. 2.1%1 day
  2. 4.5%3 days
  3. 12.3%7 days
  4. 19.3%14 days
  5. 35.6%30 days
  6. 57.8%60 days
This job: posted 33 days ago

The posting

1.参与智能体强化学习长稳训练方案的设计与研发，面向网页浏览、工具调用、代码执行、复杂任务规划及多轮交互等场景，提升智能体的任务完成能力和交互稳定性； 2.研究和优化适用于长时程智能体任务的强化学习算法，重点关注长链路信用分配、稀疏反馈、过程奖励、轨迹筛选、失败恢复和策略稳定性等问题； 3.参与智能体训练环境、任务编排、工具调用、状态管理、轨迹采集和反馈评价等基础设施建设，提升训练系统的可扩展性和实验效率； 4.参与奖励模型、环境评价器、过程监督模型和结果校验模型的研发，构建覆盖任务过程与最终结果的多粒度反馈机制； 5.负责智能体强化学习实验的设计、执行和分析，定位训练过程中的不稳定、退化、奖励投机和泛化不足等问题，并推动算法与工程方案迭代； 6.参与长稳训练相关数据集、任务集和评测体系建设，建立面向多轮交互、复杂任务执行和长期自主运行的能力评估方法； 7.跟踪智能体强化学习、环境交互学习和大模型后训练领域的前沿进展，推进相关论文复现和技术成果落地。

1.计算机科学、人工智能、数学、统计等相关专业硕士及以上学历； 2.熟悉大模型、强化学习和智能体相关技术，对文本、多模态或智能体强化学习有研究或工程实践经验； 3.理解 PPO、GRPO、RLHF、RLAIF、奖励建模、过程监督、环境反馈和多轮强化学习等相关方法； 4.熟练使用 Python、PyTorch 等语言和代码库，有 Ray、分布式训练、智能体框架、环境构建或大模型训练系统经验者优先； 5.对智能体长时程训练中的任务规划、工具使用、轨迹建模、奖励设计和训练稳定性有较强兴趣或实践经验； 6.具备较强的工程实现、实验分析和问题定位能力，能够将算法方案转化为稳定可复现的训练流程； 7.目标导向，自驱力强，具备良好的团队协作和沟通能力； 加分项：ICPC/CCPC/Kaggle/数学建模等竞赛获奖、相关方向课程项目或研究经历、高水平论文或 GitHub 开源项目。

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