美股详情

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拉夫劳伦

313.56

+4.921.59%

盘后：313.560.00000.00%19:48 EDT

成交量：104.09万

成交额：3.27亿

市值：189.94亿

市盈率：25.05

高：317.74

开：312.40

低：311.02

收：308.64

52周最高：321.77

52周最低：176.61

股本：6,057.43万

流通股本：3,807.47万

量比：1.95

换手率：2.73%

股息：3.39

股息率：1.08%

每股收益(TTM):12.52

每股收益(LYR):11.87

净资产收益率：32.55%

总资产收益率：8.62%

市净率：7.55

市盈率(LYR):26.42

数据加载中...

总览公司新闻公告

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华尔街见闻

智谱再融10亿！获上海国资押注，开源视觉大模型，能解说球赛，还会玩手机

10B级模型SOTA，超8倍参数“大”模型，智谱开源GLM-4.1V-Thinking

比音勒芬需要拉夫劳伦女孩

只用2700万参数，这个推理模型超越了DeepSeek和Claude

突破通用领域推理的瓶颈！清华NLP实验室强化学习新研究RLPR

7B模型超越DeepSeek-R1：模仿人类教师，弱模型也能教出强推理LLM

强化学习新发现：无需数学样本，仅游戏训练AI推理大增

2025年10个顶级GPU云平台：Serverless+RL开启AI普惠时代

股市刚反弹，高管们却忙着套现？什么信号？

特斯联邵岭：以多模态统一空间模型打造空间智能

掀翻传统推荐！OneRec端到端模型如何同时“吞噬”效果与成本双难题

DPO与GRPO谁更胜一筹？港中文、北大等发布首个系统性对比研究

对话特斯联邵岭：空间智能是构建世界模型的必要前提

MiniMax深夜开源！首个推理模型，4560亿参数、百万上下文、价格低至0.8元

SFT+RL双管齐下：ReasonGen-R1如何破解文生图“指令不遵”难题？

通义实验室最新成果WebDancer：自主智能Deep Research的新时代

新闻摘要-华尔街日报》--6 月 11 日

新闻摘要-华尔街日报》--6 月 11 日

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15:40","pubTimestamp":1751442000,"startTime":"0","endTime":"0","summary":"开源之外，智谱还在今天举行的智谱开放平台产业生态大会上宣布，该公司获得浦东创投集团和张江集团联合战略投资，总额10亿元。目前，开源社区缺乏一种在广泛任务范围内持续超越传统同类参数规模非推理模型的多模态推理模型。在视觉编码器部分，智谱将原始的二维卷积替换为三维卷积，尤其适用于视频理解，有效提升了处理效率。","market":"sh","thumbnail":null,"type":0,"news_type":0,"thumbnails":[],"rights":null,"property":[],"language":"zh","translate_title":"","themeId":null,"theme_name":"","theme_type":"","isJumpTheme":false,"source_url":"https://finance.sina.com.cn/stock/t/2025-07-02/doc-infeanhk2500975.shtml","is_publish_highlight":false,"source_rank":0,"column":"","sentiment":"1","news_top_title":null,"news_tag":"","news_rank":0,"length":0,"strategy_id":0,"source":"sina","symbols":["BK4588","LU0006061336.USD","RL","BK4214","BK4202","BK4585","SFT"],"gpt_icon":0},{"id":"2548597918","title":"10B级模型SOTA，超8倍参数“大”模型，智谱开源GLM-4.1V-Thinking","url":"https://stock-news.laohu8.com/highlight/detail?id=2548597918","media":"市场资讯","labels":[],"top":-1,"itemType":null,"share":"https://ttm.financial/m/news/2548597918?lang=zh_cn&edition=fundamental","pubTime":"2025-07-02 12:22","pubTimestamp":1751430120,"startTime":"0","endTime":"0","summary":"智谱正式发布 GLM-4.1V-Thinking 系列模型，并率先开源GLM-4.1V-9B-Thinking，标志着智谱 GLM 视觉大模型向高阶认知迈出了关键一步。在 18 项权威评测中，GLM-4.1V-9B-Thinking 的表现已可比肩甚至超越参数量高达 72B 的 Qwen2.5-VL-72B，充分展示出结构设计与训练策略的先进性与效率。模型原理1. 模型架构GLM-4.1V-Thinking 模型架构由三个核心模块组成：视觉编码器、多层感知机适配器以及语言解码器。2训练流程GLM-4.1V-Thinking 的训练过程分为三个阶段：预训练、监督微调 和 强化学习。","market":"other","thumbnail":null,"type":0,"news_type":0,"thumbnails":[],"rights":null,"property":[],"language":"zh","translate_title":"","themeId":null,"theme_name":"","theme_type":"","isJumpTheme":false,"source_url":"https://finance.sina.com.cn/stock/t/2025-07-02/doc-infczzsq2682912.shtml","is_publish_highlight":false,"source_rank":0,"column":"","sentiment":"0","news_top_title":null,"news_tag":"","news_rank":0,"length":0,"strategy_id":0,"source":"sina","symbols":["BK4214","RL","SFT","BK4202","BK4585","LU0006061336.USD","BK4588"],"gpt_icon":0},{"id":"2548075856","title":"比音勒芬需要拉夫劳伦女孩","url":"https://stock-news.laohu8.com/highlight/detail?id=2548075856","media":"略大参考","labels":[],"top":-1,"itemType":null,"share":"https://ttm.financial/m/news/2548075856?lang=zh_cn&edition=fundamental","pubTime":"2025-07-01 15:55","pubTimestamp":1751356501,"startTime":"0","endTime":"0","summary":"比音勒芬近来的市值缩水严重——从2023年的200亿降至不到100亿，利润也出现了自上市以来的首次负增长，谢秉政需要给焦躁的股东们一个合理解释。说这话时，比音勒芬2024年年度财报还没有出。到2025年一季报，比音勒芬营业收入12.86亿元，同比增长1.41%，归属于上市公司股东的净利润为3.31亿元，同比下滑8.47%。比音勒芬要赌一个未来，但前提是，品牌的底蕴能否撑到未来到来。因为除了“衣中茅台”外，比音勒芬还有个“外号”——中国版“拉夫劳伦”。","market":"us","thumbnail":null,"type":0,"news_type":0,"thumbnails":[],"rights":{"source":"tencent","url":"http://gu.qq.com/resources/shy/news/detail-v2/index.html#/?id=nesSN20250701155726a727e4c9&s=b","rn_cache_url":null,"customStyle":"body{padding-top:10px;}#news_title{font-weight:bold;#titleStyle#;}#news_description span{font-size:12px;#descriptionStyle#;}.footer-note{#statement#}","selectors":".mod-LoadTzbdNews, body","filters":".relate-stock, .hot-list, .recom-box, 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个训练样本，HRM在归纳基准测试和具有挑战性的符号树搜索谜题上就超越了最先进的 CoT 模型，而 CoT 模型则完全失败。HRM 采用随机初始化，无需思维链，直接根据输入完成任务。HRM 克服了这一根本限制，有效地利用其计算深度实现了近乎完美的准确率。","market":"sg","thumbnail":null,"type":0,"news_type":0,"thumbnails":[],"rights":null,"property":[],"language":"zh","translate_title":"","themeId":null,"theme_name":"","theme_type":"","isJumpTheme":false,"source_url":"https://finance.sina.com.cn/stock/t/2025-06-30/doc-infcweke9720614.shtml","is_publish_highlight":false,"source_rank":0,"column":"","sentiment":"0","news_top_title":null,"news_tag":"","news_rank":0,"length":0,"strategy_id":0,"source":"sina","symbols":["LU0211326755.USD","LU0496365809.HKD","LU1621768115.EUR","BK4202","T","ARC","LU1621767810.EUR","IE00BSNM7G36.USD","LU2384288077.EUR","IE00BFXG1179.USD","LU1621768206.USD","BK4507","RL","LU0976567544.SGD","BK4598","BK4550","BK4534","BK4195","BK4115","ACT","BK4229","BK4143","BK4588","BK4585","LU0006061336.USD","LU0211326839.USD","LU1571399168.USD","LU1621767737.EUR","LU0149725797.USD","BK4515"],"gpt_icon":0},{"id":"2546642507","title":"突破通用领域推理的瓶颈！清华NLP实验室强化学习新研究RLPR","url":"https://stock-news.laohu8.com/highlight/detail?id=2546642507","media":"市场资讯","labels":[],"top":-1,"itemType":null,"share":"https://ttm.financial/m/news/2546642507?lang=zh_cn&edition=fundamental","pubTime":"2025-06-27 10:30","pubTimestamp":1750991400,"startTime":"0","endTime":"0","summary":"针对这一关键挑战，清华大学自然语言处理实验室提出了一项关键性技术 —— 基于参考概率奖励的强化学习。同时，RLPR 提出基于奖励标准差的动态过滤机制，进一步提升强化学习的稳定性和性能提升。目前 RLPR 相关代码、模型、数据、论文均已开源。结果表明，PR 在 0.5B 规模即取得了显著优于规则奖励和验证器模型奖励的质量。RLPR 在 Gemma、Llama、Qwen 等不同基座模型上均稳定提升推理能力总结RLPR 提出了创新的 Prob-to-Reward 奖励机制，解决了现有 RLVR 范式的领域依赖问题。","market":"other","thumbnail":null,"type":0,"news_type":0,"thumbnails":[],"rights":null,"property":[],"language":"zh","translate_title":"","themeId":null,"theme_name":"","theme_type":"","isJumpTheme":false,"source_url":"https://finance.sina.com.cn/stock/t/2025-06-27/doc-infcnrzr2751855.shtml","is_publish_highlight":false,"source_rank":0,"column":"","sentiment":"0","news_top_title":null,"news_tag":"","news_rank":0,"length":0,"strategy_id":0,"source":"sina","symbols":["RL","BK4585","LU0006061336.USD","BK4202","BK4081","EMA","BK4588"],"gpt_icon":0},{"id":"2546271298","title":"7B模型超越DeepSeek-R1：模仿人类教师，弱模型也能教出强推理LLM","url":"https://stock-news.laohu8.com/highlight/detail?id=2546271298","media":"市场资讯","labels":[],"top":-1,"itemType":null,"share":"https://ttm.financial/m/news/2546271298?lang=zh_cn&edition=fundamental","pubTime":"2025-06-25 14:29","pubTimestamp":1750832940,"startTime":"0","endTime":"0","summary":"用Sanaka AI的新方法训练出的7B小模型，在传授推理技能方面，比671B的DeepSeek-R1还要有效。像人类老师一样许多高级推理模型，如DeepSeek-R1，遵循两阶段的训练过程：首先训练教师模型，然后使用其输出训练学生模型，最终产品为学生模型。竞争方法使用规模更大的模型，如DeepSeek-R1和QwQ，并结合GPT-4o-mini等工具在用于训练学生模型之前清理其输出，以获得额外帮助。","market":"fut","thumbnail":null,"type":0,"news_type":0,"thumbnails":[],"rights":null,"property":[],"language":"zh","translate_title":"","themeId":null,"theme_name":"","theme_type":"","isJumpTheme":false,"source_url":"https://finance.sina.com.cn/stock/t/2025-06-25/doc-infchkck8083801.shtml","is_publish_highlight":false,"source_rank":0,"column":"","sentiment":"0","news_top_title":null,"news_tag":"","news_rank":0,"length":0,"strategy_id":0,"source":"sina","symbols":["BK4585","RL","LU0006061336.USD","BK4202","BK4588"],"gpt_icon":0},{"id":"2545222402","title":"强化学习新发现：无需数学样本，仅游戏训练AI推理大增","url":"https://stock-news.laohu8.com/highlight/detail?id=2545222402","media":"市场资讯","labels":[],"top":-1,"itemType":null,"share":"https://ttm.financial/m/news/2545222402?lang=zh_cn&edition=fundamental","pubTime":"2025-06-24 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坐标相关的数学表现，旋转游戏则更适合角度和长度推理。","market":"us","thumbnail":null,"type":0,"news_type":0,"thumbnails":[],"rights":null,"property":[],"language":"zh","translate_title":"","themeId":null,"theme_name":"","theme_type":"","isJumpTheme":false,"source_url":"https://finance.sina.com.cn/stock/t/2025-06-24/doc-infcemnh8974111.shtml","is_publish_highlight":false,"source_rank":0,"column":"","sentiment":"0","news_top_title":null,"news_tag":"","news_rank":0,"length":0,"strategy_id":0,"source":"sina","symbols":["LU0006061336.USD","RL","BK4202","BK4214","BK4585","SFT","BK4588"],"gpt_icon":0},{"id":"2545440647","title":"2025年10个顶级GPU云平台：Serverless+RL开启AI普惠时代","url":"https://stock-news.laohu8.com/highlight/detail?id=2545440647","media":"DoNews","labels":[],"top":-1,"itemType":null,"share":"https://ttm.financial/m/news/2545440647?lang=zh_cn&edition=fundamental","pubTime":"2025-06-24 15:56","pubTimestamp":1750751801,"startTime":"0","endTime":"0","summary":"而国内同样参与者众多，比如2025年第二季度，阿里云、九章云极等企业先后发布新一代AI基础设施服务，如阿里云的FlashMoE和九章云极基于Serverless+RL架构的九章智算云Alaya NeW Cloud。在此背景下，“Serverless+RL”技术组合逐渐脱颖而出。因此，Serverless+RL的融合不仅是技术层面的进步，更是AI产业商业化的加速器。关键趋势一：GPU云厂商加速技术降本和AI开发服务升级1、九章云极：发布九章智算云Alaya NeW Cloud 2.0，开创中国Serverless+RL技术路线。","market":"us","thumbnail":null,"type":0,"news_type":0,"thumbnails":[],"rights":{"source":"tencent","url":"http://gu.qq.com/resources/shy/news/detail-v2/index.html#/?id=nesSN2025062416060495293f56&s=b","rn_cache_url":null,"customStyle":"body{padding-top:10px;}#news_title{font-weight:bold;#titleStyle#;}#news_description span{font-size:12px;#descriptionStyle#;}.footer-note{#statement#}","selectors":".mod-LoadTzbdNews, body","filters":".relate-stock, .hot-list, .recom-box, 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Model的必要前提。特斯联目前采用空间数据知识、领域模型工具，AI智能体以及空间感知模型技术相融合的方案，利用构建的知识库和系统工具，增强模型的空间感知能力。","market":"hk","thumbnail":null,"type":0,"news_type":0,"thumbnails":[],"rights":null,"property":[],"language":"zh","translate_title":"","themeId":null,"theme_name":"","theme_type":"","isJumpTheme":false,"source_url":"https://finance.sina.com.cn/stock/t/2025-06-18/doc-infamzsp5910679.shtml","is_publish_highlight":false,"source_rank":0,"column":"","sentiment":"0","news_top_title":null,"news_tag":"","news_rank":0,"length":0,"strategy_id":0,"source":"sina","symbols":["BK4588","BK4585","RL","LU0006061336.USD","BK4202"],"gpt_icon":0},{"id":"2544931382","title":"MiniMax深夜开源！首个推理模型，4560亿参数、百万上下文、价格低至0.8元","url":"https://stock-news.laohu8.com/highlight/detail?id=2544931382","media":"智东西","labels":[],"top":-1,"itemType":null,"share":"https://ttm.financial/m/news/2544931382?lang=zh_cn&edition=fundamental","pubTime":"2025-06-17 11:40","pubTimestamp":1750131644,"startTime":"0","endTime":"0","summary":"作者 | 程茜编辑 | 心缘智东西6月17日报道，今日凌晨，“大模型六小虎”之一MiniMax发布全球首个开源大规模混合架构的推理模型MiniMax-M1，并官宣了为期五天的连更计划。此外，研究人员训练了两个版本的MiniMax-M1模型，其思考预算分别为40K和80K。M1在MiniMax APP和Web上支持不限量免费使用。在这样的背景下，作为MiniMax推出的首个推理模型，MiniMax-M1正是其面对这一行业发展趋势在算法创新上的探索。","market":"us","thumbnail":null,"type":0,"news_type":0,"thumbnails":[],"rights":null,"property":[],"language":"zh","translate_title":"","themeId":null,"theme_name":"","theme_type":"","isJumpTheme":false,"source_url":"https://tech.ifeng.com/c/8kFxRzQknwS","is_publish_highlight":false,"source_rank":0,"column":"","sentiment":"0","news_top_title":null,"news_tag":"","news_rank":0,"length":0,"strategy_id":0,"source":"fenghuang_stock","symbols":["RL","BK4585","BK4202","BK4588","LU0006061336.USD"],"gpt_icon":0},{"id":"2543678781","title":"SFT+RL双管齐下：ReasonGen-R1如何破解文生图“指令不遵”难题？","url":"https://stock-news.laohu8.com/highlight/detail?id=2543678781","media":"市场资讯","labels":[],"top":-1,"itemType":null,"share":"https://ttm.financial/m/news/2543678781?lang=zh_cn&edition=fundamental","pubTime":"2025-06-16 17:22","pubTimestamp":1750065720,"startTime":"0","endTime":"0","summary":"ReasonGen-R1 使得自回归图像生成模型可以端到端地在输出图片之前先进行文本“思考”，大幅提升了基座模型的语义遵循能力，并在多个语义指标上取得突破。目前，ReasonGen-R1 已全面开源。方法概览ReasonGen-R1 的训练包括两个核心阶段：监督微调阶段以及强化学习阶段。","market":"sg","thumbnail":null,"type":0,"news_type":0,"thumbnails":[],"rights":null,"property":[],"language":"zh","translate_title":"","themeId":null,"theme_name":"","theme_type":"","isJumpTheme":false,"source_url":"https://finance.sina.com.cn/stock/t/2025-06-16/doc-infahksh9599543.shtml","is_publish_highlight":false,"source_rank":0,"column":"","sentiment":"0","news_top_title":null,"news_tag":"","news_rank":0,"length":0,"strategy_id":0,"source":"sina","symbols":["SFT","RL","BK4202","BK4588","BK4585","LU0006061336.USD","BK4214"],"gpt_icon":0},{"id":"2542601115","title":"通义实验室最新成果WebDancer：自主智能Deep Research的新时代","url":"https://stock-news.laohu8.com/highlight/detail?id=2542601115","media":"市场资讯","labels":[],"top":-1,"itemType":null,"share":"https://ttm.financial/m/news/2542601115?lang=zh_cn&edition=fundamental","pubTime":"2025-06-12 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