Загрузка видео...

Не удалось загрузить видео

Возникла проблема при загрузке этого видео. Это может быть связано с временными проблемами сети или видео может быть недоступно.

На главную

1/4 How to get the most from a few high‑quality, trusted examples—not by piling up data? Tongyi Lab shifts from data‑centric to sample‑centric and presents LPPO, a Progressive Optimization RL framework to break reasoning bottlenecks: master each problem, not just add more.

Tongyi Lab

26,160 subscribers

1,080,971 просмотров • 1 год назад •via X (Twitter)

Образование Новости и политика Наука и технологии

Anya Rossi• Live Now

Private livecam show

Комментарии: 0

Нет доступных комментариев

Здесь появятся комментарии из оригинального поста

Похожие видео

It's easy for robots to nail a demo. It's much harder to get them to work reliably in the real world. The bottleneck is data. At Axis Robotics we're building a compounding data engine that combines simulation, ego-centric data and DAgger correction data at scale. Watch our CEO ChrisF(✱,✱) break down the data bottleneck, why messy data can still train good models, and what it takes to get robots out of the lab with blocmates. ⬇️

It's easy for robots to nail a demo. It's much harder to get them to work reliably in the real world. The bottleneck is data. At Axis Robotics we're building a compounding data engine that combines simulation, ego-centric data and DAgger correction data at scale. Watch our CEO ChrisF(✱,✱) break down the data bottleneck, why messy data can still train good models, and what it takes to get robots out of the lab with blocmates. ⬇️

Axis Robotics

22,634 просмотров • 24 дней назад

Current world models often separate spatial reasoning from temporal modeling, leading to consistency bottlenecks and scaling limits. We developed RAYNOVA, a 4D world foundation model, to address this by unifying space and time into a single representation via a pure auto-regressive framework. The result: Data-driven high-fidelity simulation data that scales across any vehicles, anywhere. Read the full technical breakdown:

Current world models often separate spatial reasoning from temporal modeling, leading to consistency bottlenecks and scaling limits. We developed RAYNOVA, a 4D world foundation model, to address this by unifying space and time into a single representation via a pure auto-regressive framework. The result: Data-driven high-fidelity simulation data that scales across any vehicles, anywhere. Read the full technical breakdown:

Applied Intuition

11,252 просмотров • 3 месяцев назад

Congrats to David AI $25 M Series A! a story on how, during the YC batch, Tomer & Ben got from a niche problem in robotics to solving a big problem and becoming the first audio research data lab, building the “Common Crawl for audio" 1/6

Congrats to David AI $25 M Series A! a story on how, during the YC batch, Tomer & Ben got from a niche problem in robotics to solving a big problem and becoming the first audio research data lab, building the “Common Crawl for audio" 1/6

Diana

49,479 просмотров • 1 год назад

most people stick to just 1 or 2 tables they are familiar with when writing their SQL queries in we built the ability to search for tables, preview tables and easily sample rows from a table so you can find the ones you need + even discover new tables data discovery is a big problem even in small companies. most data doesn't get analyzed because most of company doesn't know it exists

most people stick to just 1 or 2 tables they are familiar with when writing their SQL queries in we built the ability to search for tables, preview tables and easily sample rows from a table so you can find the ones you need + even discover new tables data discovery is a big problem even in small companies. most data doesn't get analyzed because most of company doesn't know it exists

rahul

11,550 просмотров • 1 год назад

Implementing motion imitation methods involves lots of nuisances. Not many codebases get all the details right. So, we're excited to release MimicKit! A framework with high quality implementations of our methods: DeepMimic, AMP, ASE, ADD, and more to come!

Implementing motion imitation methods involves lots of nuisances. Not many codebases get all the details right. So, we're excited to release MimicKit! A framework with high quality implementations of our methods: DeepMimic, AMP, ASE, ADD, and more to come!

Jason Peng

170,627 просмотров • 11 месяцев назад

Introducing: Advanced Data Assets™ By LayerAI 🧬 Data is used to power Big Data and AI. The increasing demand for high quality data to improve AI models will lead to new companies creating value in the global data market. The problem: current data collection methods make data fall short of its true potential. Data is limited. Building a decentralized solution where users choose to productize and earn from their data opens untapped sources of income. 🌐 Creating Advanced Data Assets™ There exists a market opportunity to create a digital blockchain-based global data marketplace. Data collected using our nodes, our native network apps, settled on LayerAI. The Advanced Data. Meet the Ecosystem that empowers users to participate in the Data Economy.

Introducing: Advanced Data Assets™ By LayerAI 🧬 Data is used to power Big Data and AI. The increasing demand for high quality data to improve AI models will lead to new companies creating value in the global data market. The problem: current data collection methods make data fall short of its true potential. Data is limited. Building a decentralized solution where users choose to productize and earn from their data opens untapped sources of income. 🌐 Creating Advanced Data Assets™ There exists a market opportunity to create a digital blockchain-based global data marketplace. Data collected using our nodes, our native network apps, settled on LayerAI. The Advanced Data. Meet the Ecosystem that empowers users to participate in the Data Economy.

LayerAI | AI2Earn

27,136 просмотров • 2 лет назад

Imitation learning is great, but needs us to have (near) optimal data. We throw away most other data (failures, evaluation data, suboptimal data, undirected play data), even though this data can be really useful and way cheaper! In our new work - RISE, we show a simple way to *use all of this non-optimal data to robustify imitation learning* with minimal requirements beyond BC. Key idea: use non-expert data to learn how to *recover* back to expert data with a minimal frills offline RL that works under sparse data coverage. Allows usage of *all* available data, not just expert data - never throw your data away! Paper: Website: A 🧵(1/10)

Imitation learning is great, but needs us to have (near) optimal data. We throw away most other data (failures, evaluation data, suboptimal data, undirected play data), even though this data can be really useful and way cheaper! In our new work - RISE, we show a simple way to use all of this non-optimal data to robustify imitation learning with minimal requirements beyond BC. Key idea: use non-expert data to learn how to recover back to expert data with a minimal frills offline RL that works under sparse data coverage. Allows usage of all available data, not just expert data - never throw your data away! Paper: Website: A 🧵(1/10)

Abhishek Gupta

20,635 просмотров • 10 месяцев назад

💡Divergence thinking💡 is a hallmark of human creativity and problem-solving 🤖Can LLMs also do divergent reasoning to generate diverse solutions🤔? Introducing Flow-of-Reasoning (FoR) 🌊, a data-efficient way of training LLM policy to generate diverse, high-quality reasoning trajectories Unlike existing RL (like PPO) and planning (like MCTS) to find the max-reward trajectory (akin to convergent thinking), FoR connects LLM reasoning with the #GFlowNet formulation and enables LLMs to find trajectories proportional to reward distribution. 🎬The demo video illustrates how FoR learns and infers multiple solutions to a ♠️Game24 puzzle. 🎯Inferring for diverse solutions could be useful for robustness, data augmentation, and enhanced model generalization. Project page: Paper: Github:

💡Divergence thinking💡 is a hallmark of human creativity and problem-solving 🤖Can LLMs also do divergent reasoning to generate diverse solutions🤔? Introducing Flow-of-Reasoning (FoR) 🌊, a data-efficient way of training LLM policy to generate diverse, high-quality reasoning trajectories Unlike existing RL (like PPO) and planning (like MCTS) to find the max-reward trajectory (akin to convergent thinking), FoR connects LLM reasoning with the #GFlowNet formulation and enables LLMs to find trajectories proportional to reward distribution. 🎬The demo video illustrates how FoR learns and infers multiple solutions to a ♠️Game24 puzzle. 🎯Inferring for diverse solutions could be useful for robustness, data augmentation, and enhanced model generalization. Project page: Paper: Github:

Lianhui Qin

50,493 просмотров • 2 лет назад

Model progress is no longer constrained by architecture, but by access to high-quality, human-generated data. Scraped internet data is finite, low-signal and increasingly synthetic. Kled AI pays real people to upload authentic, real world content task by task in their mobile app. We are super excited to back Avi Patel and Kled AI as they build a massive data marketplace. If you are in need of a great data partner, reach out! More from us here (we were not investors when we filmed):

Model progress is no longer constrained by architecture, but by access to high-quality, human-generated data. Scraped internet data is finite, low-signal and increasingly synthetic. Kled AI pays real people to upload authentic, real world content task by task in their mobile app. We are super excited to back Avi Patel and Kled AI as they build a massive data marketplace. If you are in need of a great data partner, reach out! More from us here (we were not investors when we filmed):

Nichole Wischoff

37,109 просмотров • 5 месяцев назад

We’re pleased to share our proof-of-concept demo showcasing Autonomys' decentralized triple-stack solution—Permanent DSN, Data Access, and Private Compute. This demo features: 🗜️ RAG workflows secured in a Trusted Execution Environment (TEE) 🔏 Privacy-preserving AI deployment from securely stored data ➕ More See how Autonomys enables developers to build scalable, customizable, and human-centric AI super dApps. 📺 Watch now: And follow along with our explainer article: 🔗

We’re pleased to share our proof-of-concept demo showcasing Autonomys' decentralized triple-stack solution—Permanent DSN, Data Access, and Private Compute. This demo features: 🗜️ RAG workflows secured in a Trusted Execution Environment (TEE) 🔏 Privacy-preserving AI deployment from securely stored data ➕ More See how Autonomys enables developers to build scalable, customizable, and human-centric AI super dApps. 📺 Watch now: And follow along with our explainer article: 🔗

Autonomys | AI3.0

105,265 просмотров • 1 год назад

Mark Cuban: A lot of these data centers are going to end up as pickleball courts. “Everybody thinks there’s going to be so much more utilization and there will be… but there’s going to be technological breakthroughs as well. Just like we saw with fiber. It went from 1GB to 10 to 100GB and then there wasn’t a fiber problem anymore. How is it not going to be the case that we don't get the same price performance improvements on the AI side and on the data center side?”

Mark Cuban: A lot of these data centers are going to end up as pickleball courts. “Everybody thinks there’s going to be so much more utilization and there will be… but there’s going to be technological breakthroughs as well. Just like we saw with fiber. It went from 1GB to 10 to 100GB and then there wasn’t a fiber problem anymore. How is it not going to be the case that we don't get the same price performance improvements on the AI side and on the data center side?”

jay plemons

307,169 просмотров • 1 месяц назад

With enough data, robots and AI can learn “world models” that let them predict the results of their actions. These models are a way to learn how embodied AI agents can perform a wide variety of useful tasks — but they require a huge amount of data. The team at General Intuition General Intuition has a solution: use data from video games! Games teach movement, problem solving, and complex spatial reasoning, and they come in a staggering diversity of forms, covering a wide variety of problems. What’s more, the captured data is high-quality, without the noise or annotation error that can come from We sat down with Pim de Witte and Adam Jelley from the General Intuition team to learn more about their history, their plans, and their philosophy.

With enough data, robots and AI can learn “world models” that let them predict the results of their actions. These models are a way to learn how embodied AI agents can perform a wide variety of useful tasks — but they require a huge amount of data. The team at General Intuition General Intuition has a solution: use data from video games! Games teach movement, problem solving, and complex spatial reasoning, and they come in a staggering diversity of forms, covering a wide variety of problems. What’s more, the captured data is high-quality, without the noise or annotation error that can come from We sat down with Pim de Witte and Adam Jelley from the General Intuition team to learn more about their history, their plans, and their philosophy.

RoboPapers

85,927 просмотров • 9 месяцев назад

There’s plenty to debate on data centers… But the data moratoriums taking over the country are driven by politics, not facts. The most advanced data centers use zero water and continue to become more energy efficient. And when data centers are banned… communities walk away from lower taxes, funded schools, and loads of good paying jobs. That's the trade off…

There’s plenty to debate on data centers… But the data moratoriums taking over the country are driven by politics, not facts. The most advanced data centers use zero water and continue to become more energy efficient. And when data centers are banned… communities walk away from lower taxes, funded schools, and loads of good paying jobs. That's the trade off…

Tim Young

11,079 просмотров • 1 месяц назад

Inferact Co-founder Simon Mo on the trajectory of open and closed models: "What really differentiates open-weight models from closed-weight models, right? In the end, there's not much differentiation. It's more about the distribution strategy and go-to-market strategy... Capability-wise, I don't really see a big gap, not even today." "It's not about just source data. It's not about where to get the data from. Rather, it's who can build the best environment and who can make the most optimization and algorithmic choices to leverage all this learning from this environment." "So the next year is all gonna be about that. It's about how open-weight model labs are differentiating and really getting the model to meet the real world." "Project out in a year, there's not gonna be any difference." Simon Mo

Inferact Co-founder Simon Mo on the trajectory of open and closed models: "What really differentiates open-weight models from closed-weight models, right? In the end, there's not much differentiation. It's more about the distribution strategy and go-to-market strategy... Capability-wise, I don't really see a big gap, not even today." "It's not about just source data. It's not about where to get the data from. Rather, it's who can build the best environment and who can make the most optimization and algorithmic choices to leverage all this learning from this environment." "So the next year is all gonna be about that. It's about how open-weight model labs are differentiating and really getting the model to meet the real world." "Project out in a year, there's not gonna be any difference." Simon Mo

a16z

33,782 просмотров • 28 дней назад

Most Americans don’t realize the incredible amount of private data that is being harvested from their smartphones by third parties, says Joe Weil, CEO of Unplugged Unplugged. What’s even worse is that this advertising data is not protected by the Fourth Amendment, he says. Data brokers sell this data freely, and just by applying a few filters, anyone—foreign governments, intelligence services, criminal cartels—can easily triangulate the data to surveil and target individual people or groups of people.

Most Americans don’t realize the incredible amount of private data that is being harvested from their smartphones by third parties, says Joe Weil, CEO of Unplugged Unplugged. What’s even worse is that this advertising data is not protected by the Fourth Amendment, he says. Data brokers sell this data freely, and just by applying a few filters, anyone—foreign governments, intelligence services, criminal cartels—can easily triangulate the data to surveil and target individual people or groups of people.

American Thought Leaders 🇺🇸 with @JanJekielek

23,104 просмотров • 9 месяцев назад

#WATCH | Delhi | Principal Secretary to PM, Dr PK Mishra says, "The Indian statistical system, which has a glorious past, saw some decline for a few years, but the system itself and others have been able to revive it. Also, the Prime Minister's office played a catalyst and a facilitator. The way we have now improved our system, I'm sure it will again become very well established and predominant globally." "A lot of data is generated by our departments, ministries and digital activities also. So, how to use the potential of the data, how to standardise it, how to make it compatible with each other, how to derive inferences from the data, how to ensure that the data is as trusted as comprehensive and as compatible as the earlier survey data or maybe it is expanding the scope of our statistics."

#WATCH | Delhi | Principal Secretary to PM, Dr PK Mishra says, "The Indian statistical system, which has a glorious past, saw some decline for a few years, but the system itself and others have been able to revive it. Also, the Prime Minister's office played a catalyst and a facilitator. The way we have now improved our system, I'm sure it will again become very well established and predominant globally." "A lot of data is generated by our departments, ministries and digital activities also. So, how to use the potential of the data, how to standardise it, how to make it compatible with each other, how to derive inferences from the data, how to ensure that the data is as trusted as comprehensive and as compatible as the earlier survey data or maybe it is expanding the scope of our statistics."

ANI

87,255 просмотров • 2 месяцев назад

So I heard we need more data for robot learning :) Purely real world teleop is expensive and slow, making large scale data collection challenging. I’ve been excited about getting more data into robot learning, going beyond just real-world teleop data. To this end, we’ve been scaling up data generation with RL in realistic simulations generated on the fly from crowdsourced videos. Enables realistic data collection, much more cheaply than purely real world teleop. Importantly, data collection becomes even*cheaper* with more environments, allowing training with over 100x more data. Transfers to real robots for generalizable manipulation. A 🧵 (1/N)

So I heard we need more data for robot learning :) Purely real world teleop is expensive and slow, making large scale data collection challenging. I’ve been excited about getting more data into robot learning, going beyond just real-world teleop data. To this end, we’ve been scaling up data generation with RL in realistic simulations generated on the fly from crowdsourced videos. Enables realistic data collection, much more cheaply than purely real world teleop. Importantly, data collection becomes evencheaper with more environments, allowing training with over 100x more data. Transfers to real robots for generalizable manipulation. A 🧵 (1/N)

Abhishek Gupta

13,358 просмотров • 1 год назад

A model is only as good as its data, and we’ve long since exhausted the internet. From here on out, model progress is gated by data production. ’s Brendan (can/do) joined us at our Sovereign AI event to talk about how RL environments get built, and why your data might be your real moat: 00:00 Introduction 00:47 A short history of the data market: crowdsourcing to agentic data 02:29 What an RL environment is: worlds, apps, tasks 03:57 Why only humans can measure the frontier 05:35 Building verifiers is the hard part 06:44 Walkthrough: a real legal RL environment 08:18 Leaderboards — and what open weights change 09:45 Post-training results on Apex Agents 11:17 Three ways companies buy data 12:49 Q&A: How do you price data? 14:17 Q&A: What "data quality" actually means 16:42 Q&A: The misunderstanding about synthetic data 18:17 Q&A: Why RL environments now — and what comes after 21:20 Q&A: Can you scale rubric generation with models? 23:00 Q&A: RL environments for cyber defense 25:33 Q&A: Build data in-house or partner?

A model is only as good as its data, and we’ve long since exhausted the internet. From here on out, model progress is gated by data production. ’s Brendan (can/do) joined us at our Sovereign AI event to talk about how RL environments get built, and why your data might be your real moat: 00:00 Introduction 00:47 A short history of the data market: crowdsourcing to agentic data 02:29 What an RL environment is: worlds, apps, tasks 03:57 Why only humans can measure the frontier 05:35 Building verifiers is the hard part 06:44 Walkthrough: a real legal RL environment 08:18 Leaderboards — and what open weights change 09:45 Post-training results on Apex Agents 11:17 Three ways companies buy data 12:49 Q&A: How do you price data? 14:17 Q&A: What "data quality" actually means 16:42 Q&A: The misunderstanding about synthetic data 18:17 Q&A: Why RL environments now — and what comes after 21:20 Q&A: Can you scale rubric generation with models? 23:00 Q&A: RL environments for cyber defense 25:33 Q&A: Build data in-house or partner?

Pat Grady

65,994 просмотров • 23 дней назад

Scale alone is not enough for AI data. Quality and complexity are equally critical. Excited to support all of these for LLM developers with Snorkel AI Data-as-a-Service, and to share our new leaderboard! — Our decade-plus of research and work in AI data has a simple point: scale alone is not enough. AI success is all about the quality, complexity, and distribution of data—in addition to volume. We’re excited to be powering leading LLM developers with Snorkel AI Expert Data-as-a-Service, our white glove service for custom, expert-level AI datasets—and to now preview some of what we’re building via our new Expert Data Leaderboard (🔗 in 🧵) + upcoming OSS dataset releases! Snorkel Expert Data-as-a-Service is built to meet the rapidly evolving data needs of the agentic AI world—where success is built on the quality, complexity, and distribution of datasets, in addition to size and scale. This kind of high-quality, frontier AI data can only come from a union of technology and human expertise. With Snorkel Expert Data-as-a-Service, we’re powering frontier LLM developers across agentic, expert knowledge, reasoning, coding, multi-modal, and other task types via the combination of these two key components: - (1) The Snorkel Expert Network: A global team of subject matter experts focused wholly on specialized knowledge–spanning thousands of topics in STEM/academic, vertical/professional, and consumer/lifestyle domains. - (2) Snorkel AI Data Development Platform: Our unique programmatic data curation and quality control platform, accelerating and improving expert authoring and review through principled techniques developed over the last decade of R&D. Now: we’re incredibly excited to showcase some of the power of Snorkel Expert Data-as-a-Service via the new Snorkel Leaderboard—putting frontier models to the test in complex, agentic, and reasoning settings inspired by real industry scenarios (not esoteric puzzles)! We’ll be releasing new leaderboards and accompanying expert-verified open source datasets (coming soon!) regularly. To start, we’re sharing three initial ones in preview: - SnorkelFinance: Q&A over financial documents requiring agentic tool-calling and reasoning - SnorkelUnderwrite: Agentic insurance tasks requiring industry-specific reasoning and tool use - SnorkelSequences: Mathematical tasks requiring compositional multi-step reasoning

Scale alone is not enough for AI data. Quality and complexity are equally critical. Excited to support all of these for LLM developers with Snorkel AI Data-as-a-Service, and to share our new leaderboard! — Our decade-plus of research and work in AI data has a simple point: scale alone is not enough. AI success is all about the quality, complexity, and distribution of data—in addition to volume. We’re excited to be powering leading LLM developers with Snorkel AI Expert Data-as-a-Service, our white glove service for custom, expert-level AI datasets—and to now preview some of what we’re building via our new Expert Data Leaderboard (🔗 in 🧵) + upcoming OSS dataset releases! Snorkel Expert Data-as-a-Service is built to meet the rapidly evolving data needs of the agentic AI world—where success is built on the quality, complexity, and distribution of datasets, in addition to size and scale. This kind of high-quality, frontier AI data can only come from a union of technology and human expertise. With Snorkel Expert Data-as-a-Service, we’re powering frontier LLM developers across agentic, expert knowledge, reasoning, coding, multi-modal, and other task types via the combination of these two key components: - (1) The Snorkel Expert Network: A global team of subject matter experts focused wholly on specialized knowledge–spanning thousands of topics in STEM/academic, vertical/professional, and consumer/lifestyle domains. - (2) Snorkel AI Data Development Platform: Our unique programmatic data curation and quality control platform, accelerating and improving expert authoring and review through principled techniques developed over the last decade of R&D. Now: we’re incredibly excited to showcase some of the power of Snorkel Expert Data-as-a-Service via the new Snorkel Leaderboard—putting frontier models to the test in complex, agentic, and reasoning settings inspired by real industry scenarios (not esoteric puzzles)! We’ll be releasing new leaderboards and accompanying expert-verified open source datasets (coming soon!) regularly. To start, we’re sharing three initial ones in preview: - SnorkelFinance: Q&A over financial documents requiring agentic tool-calling and reasoning - SnorkelUnderwrite: Agentic insurance tasks requiring industry-specific reasoning and tool use - SnorkelSequences: Mathematical tasks requiring compositional multi-step reasoning

Alex Ratner

495,851 просмотров • 1 год назад