Skip to content

Translation in progress Some entries still include text in the original language.

Oct 12 · Monday

All papers

Found 5 papers
Filter4
Select any value within a category; different categories must all match. Counts include the other filters you have selected.Another 233papers have no tags yet and are not included in filtered results.

Another 233papers do not have category tags yet and are not included in filtered results.

  1. OtherarXiv 2610.11959

    MiMo-V2.6: Toward Self-Improvement via Scaled Reinforcement Learning

    Extending reinforcement learning for full-modality foundation models to support agent self-improvement

    Published
    Evaluated models
    MiMo-V2.6-Pro, MiMo-V2.6-Flash, MiMo-V2.6-Distill-Qwen-9B
    AbstractThe series uses three-dimensional RL and group scoring to improve agent scores; the authors claim the final performance is comparable to frontier models.

    MiMo-V2.6 is an all-modality MoE technical report, pursuing agent self-improvement by scaling up RL computation, environments, and scoring.

    Paper analysisRead analysis
  2. OtherarXiv 2609.34426

    Q-learning Penalized Transformer(QPT):面向安全离线强化学习的训练-推理一致框架

    提出 QPT,用奖励与成本 Q 惩罚训练满足约束的离线强化学习策略

    Published
    AbstractQPT 把条件 Transformer 与奖励/成本 Q 惩罚连成训练–推理一致的安全离线 RL 方法。

    QPT 针对安全离线 RL:只从离线数据学习,同时处理约束满足、回报最大化和行为正则,并希望部署时更换成本阈值而无需重训。

    Paper analysisRead analysis
  3. OtherarXiv 2607.28568

    Frontis-MA1:面向机器学习工程递归自我改进的 AI4AI 模型

    提出 OpenMLE,后训练元进化智能体并做机器学习工程长程搜索

    Published
    Evaluated models
    Frontis-MA1-35B, Frontis-MA1-30B
    AbstractOpenMLE 训练 Frontis-MA1-35B,用四个共享算子把 MLE 后训练和长程搜索接成元进化闭环。

    作者提出全栈 OpenMLE,在机器学习工程里研究从进化到元进化的一步,并把递归自我改进视为更远目标。智能体要在延迟、有噪声的执行反馈下反复修改程序。作者认为公开系统很少同时提供可扩展环境、基于执行的后训练,以及部署该模型的长程进化搜索。

    Paper analysisRead analysis
You have reached the end