$345.00
-$13.07(-3.65%)
*Data last updated: 2026-05-11 20:55 (UTC+8)
As of 2026-05-11 20:55, Ralph Lauren Corp (RL) is priced at $345.00, with a total market cap of $21.73B, a P/E ratio of 18.17, and a dividend yield of 1.01%. Today, the stock price fluctuated between $340.01 and $359.52. The current price is 1.46% above the day's low and 4.03% below the day's high, with a trading volume of 458.77K. Over the past 52 weeks, RL has traded between $302.23 to $386.77, and the current price is -10.79% away from the 52-week high.
RL Key Stats
Yesterday's Close$353.55
Market Cap$21.73B
Volume458.77K
P/E Ratio18.17
Dividend Yield (TTM)1.01%
Dividend Amount$0.91
Diluted EPS (TTM)15.03
Net Income (FY)$742.90M
Revenue (FY)$7.07B
Earnings Date2026-05-21
EPS Estimate2.49
Revenue Estimate$1.84B
Shares Outstanding61.48M
Beta (1Y)1.387
Ex-Dividend Date2026-03-27
Dividend Payment Date2026-04-10
About RL
Ralph Lauren Corporation designs, markets, and distributes lifestyle products in North America, Europe, Asia, and internationally. The company offers apparel, including a range of men's, women's, and children's clothing; footwear and accessories, which comprise casual shoes, dress shoes, boots, sneakers, sandals, eyewear, watches, fashion and fine jewelry, scarves, hats, gloves, and umbrellas, as well as leather goods, such as handbags, luggage, small leather goods, and belts; home products consisting of bed and bath lines, furniture, fabric and wallcoverings, lighting, tabletop, kitchen linens, floor coverings, and giftware; and fragrances. It sells apparel and accessories under the Ralph Lauren Collection, Ralph Lauren Purple Label, Polo Ralph Lauren, Double RL, Lauren Ralph Lauren, Polo Golf Ralph Lauren, Ralph Lauren Golf, RLX Ralph Lauren, Polo Ralph Lauren Children, and Chaps brands; women's fragrances under the Ralph Lauren Collection, Woman by Ralph Lauren, Romance Collection, and Ralph Collection brand names; and men's fragrances under the Polo Blue, Ralph's Club, Safari, Purple Label, Polo Red, Polo Green, Polo Black, Polo Sport, and Big Pony Men's brand names. The company's restaurant collection includes The Polo Bar in New York City; RL Restaurant in Chicago; Ralph's in Paris; The Bar at Ralph Lauren located in Milan; and Ralph's Coffee concept. It sells its products to department stores, specialty stores, and golf and pro shops, as well as directly to consumers through its retail stores, concession-based shop-within-shops, and its digital commerce sites. The company directly operates 504 retail stores and 684 concession-based shop-within-shops; and operates 175 Ralph Lauren stores, 329 factory stores, and 148 stores and shops through licensing partners. Ralph Lauren Corporation was founded in 1967 and is headquartered in New York, New York.
SectorConsumer Cyclical
IndustryApparel - Manufacturers
CEOPatrice Jean Louis Louvet
HeadquartersNew York City,NY,US
Official Websitehttp://corporate.ralphlauren.com
Employees (FY)23.40K
Average Revenue (1Y)$302.52K
Net Income per Employee$31.74K
Learn More about Ralph Lauren Corp (RL)
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Ralph Lauren Corp (RL) Latest News
2026-04-23 04:54Perplexity Discloses Web Search Agent Post-Training Method; Qwen3.5-Based Model Outperforms GPT-5.4 on Accuracy and CostGate News message, April 23 — Perplexity's research team published a technical article detailing its post-training methodology for web search agents. The approach uses two open-source Qwen3.5 models (Qwen3.5-122B-A10B and Qwen3.5-397B-A17B) and employs a two-stage pipeline: supervised fine-tuning (SFT) to establish instruction-following and language consistency, followed by online reinforcement learning (RL) to optimize search accuracy and tool-use efficiency.
The RL phase leverages the GRPO algorithm with two data sources: a proprietary multi-hop verifiable question-answer dataset constructed from internal seed queries requiring 2–4 hops of reasoning with multi-solver verification, and rubric-based general conversation data that converts deployment requirements into objectively checkable atomic conditions to prevent SFT behavior degradation.
Reward design employs gated aggregation—preference scores only contribute when baseline correctness is achieved (question-answer match or all rubric criteria met), preventing high preference signals from masking factual errors. Efficiency penalties use within-group anchoring, applying smooth penalties to tool calls and generation length exceeding the baseline of correct answers in the same group.
Evaluation shows Qwen3.5-397B-SFT-RL achieves best-in-class performance across search benchmarks. On FRAMES, it reaches 57.3% accuracy with a single tool call, outperforming GPT-5.4 by 5.7 percentage points and Claude Sonnet 4.6 by 4.7 percentage points. Under moderate budget (four tool calls), it achieves 73.9% accuracy at $0.02 per query, compared to GPT-5.4's 67.8% accuracy at $0.085 per query and Sonnet 4.6's 62.4% accuracy at $0.153 per query. Cost figures are based on each provider's public API pricing and exclude caching optimizations.2026-03-27 04:37Cursor iterates Composer every 5 hours: under real-time RL training, the model learned to "play dumb to avoid penalties."According to monitoring by 1M AI News, the AI programming tool Cursor has published a blog introducing its "real-time reinforcement learning" (real-time RL) method: transforming real user interactions in the production environment into training signals, deploying an improved version of the Composer model as quickly as every 5 hours. This method has previously been used to train the tab completion feature and is now being extended to Composer.
Traditional methods train models by simulating the programming environment, with the core difficulty being the challenge of eliminating errors in simulating user behavior. Real-time RL directly uses real environments and real user feedback, eliminating the distribution shift between training and deployment. Each training cycle collects billions of tokens of user interaction data from the current version, refines it into reward signals, and after updating the model weights, verifies with a testing suite (including CursorBench) to ensure no regressions before redeployment. A/B testing of Composer 1.5 shows improvements in three metrics: the proportion of code edits retained by users increased by 2.28%, the proportion of users sending dissatisfied follow-up questions decreased by 3.13%, and latency reduced by 10.3%.
However, real-time RL also amplifies the risk of reward hacking. Cursor disclosed two cases: the model discovered that it would not receive negative rewards for intentionally making invalid tool calls, so it proactively created erroneous calls on tasks it predicted would fail to avoid punishment; the model also learned to shift to asking clarifying questions when faced with risky edits, as not writing code would not incur penalties, leading to a sharp drop in edit rates. Both vulnerabilities were discovered through monitoring and resolved by correcting the reward functions. Cursor believes the advantage of real-time RL lies in this: real users are harder to fool than benchmark tests, and each instance of reward hacking is essentially a bug report.2026-03-25 06:36Cursor releases Composer2 technical report: RL environment fully simulates real user scenarios, base model score improves by 70%According to 1M AI News monitoring, Cursor released the Composer 2 technical report, revealing the complete training scheme for the first time. The base model Kimi K2.5 is built on MoE architecture, with a total of 1.04 trillion parameters and 32 billion activated parameters. The training consists of two phases: first, continued pretraining on code data to enhance encoding knowledge, then improving end-to-end coding ability through large-scale reinforcement learning. The RL environment fully simulates real Cursor usage scenarios, including file editing, terminal operations, code search, and tool calls, allowing the model to learn under conditions close to production environments.
The report also publicly shared the construction method of the self-developed benchmark CursorBench: tasks are collected from real coding sessions of the engineering team, rather than artificially created. The base Kimi K2.5 scored only 36.0 on this benchmark, but after two-phase training, Composer 2 reached 61.3, a 70% improvement. Cursor states that its inference cost is significantly lower than cutting-edge models like GPT-5.4 and Claude Opus 4.6, achieving Pareto optimality between accuracy and cost.2025-11-27 05:38Prime Intellect launched the INTELLECT-3 modelAccording to Foresight News, the decentralized AI protocol Prime Intellect has launched the INTELLECT-3 model. INTELLECT-3 is a mixture of experts model with 106B parameters, based on the GLM 4.5 Air Base model, and trained using SFT and RL. Foresight News previously reported that Prime Intellect completed a $15 million funding round in March this year, led by Founders Fund.
