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PPO has long dominated robot locomotion training in simulation. SAC, despite its sample efficiency, couldn't keep up. We analyze why: 🔗 🔥Integrated into RSL-RL, our approach requires only minimal changes, making SAC a drop-in alternative out of the box.

Robotic Systems Lab

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44,817 次观看 • 2 个月前 •via X (Twitter)

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Model-Free Reinforcement Learning (MFRL) has been alluring, especially with supercharged compute with physics on GPU. However, the methods use 0-th order gradients, and are often not the best optimizers. Can we do better than PPO in continuous control for robotics? Turns out yes! 🥳 tl;dr: Faster, better RL than PPO in continuous control 💪 The answer lies in using more information from the simulation. We are juicing the simulation on GPU as it is, why not use it for gradients as well? This has been a driving question in a series of our works. We first studied this problem in ICLR 2022 paper on Short Horizon Actor Critic Naive gradient based methods are stuck in local minima and have exploding/vanishing gradients. SHAC solved this problem truncated rollouts and model based value estimation, where the model is Differentiable Sim. This boosted sample efficiency and wall-clock time immensely especially in high dimensional systems such as humanoids Yet, given enough compute PPO often caught up. Our follow up paper on on Adaptive Horizon Actor Critic at ICML 2024 discovers the cause and provides a fix. However, we find that even when given ground-truth dynamics, not all gradients are useful due to sample error. 1st-Order Model-Based Reinforcement Learning methods employing differentiable simulation provide gradients with reduced variance but are susceptible to bias in scenarios involving stiff dynamics, such as physical contact. We find that back-propagating through contact and long trajectories drastically reduces gradient accuracy. Using this insight, we propose AHAC to dynamically adapt its roll-out horizon to avoid differentiating through stiff contact. AHAC is a first-order model-based RL algorithm that learns high-dimensional tasks in minutes (wall clock) and outperforms PPO by 40%, even in the limit of data provided to PPO. This work is led by Ignat Georgiev alongside Krishnan Srinivasan, Jie Xu, Eric Heiden and ample assistance from warp team at NVIDIA Robotics (Miles Macklin)

Model-Free Reinforcement Learning (MFRL) has been alluring, especially with supercharged compute with physics on GPU. However, the methods use 0-th order gradients, and are often not the best optimizers. Can we do better than PPO in continuous control for robotics? Turns out yes! 🥳 tl;dr: Faster, better RL than PPO in continuous control 💪 The answer lies in using more information from the simulation. We are juicing the simulation on GPU as it is, why not use it for gradients as well? This has been a driving question in a series of our works. We first studied this problem in ICLR 2022 paper on Short Horizon Actor Critic Naive gradient based methods are stuck in local minima and have exploding/vanishing gradients. SHAC solved this problem truncated rollouts and model based value estimation, where the model is Differentiable Sim. This boosted sample efficiency and wall-clock time immensely especially in high dimensional systems such as humanoids Yet, given enough compute PPO often caught up. Our follow up paper on on Adaptive Horizon Actor Critic at ICML 2024 discovers the cause and provides a fix. However, we find that even when given ground-truth dynamics, not all gradients are useful due to sample error. 1st-Order Model-Based Reinforcement Learning methods employing differentiable simulation provide gradients with reduced variance but are susceptible to bias in scenarios involving stiff dynamics, such as physical contact. We find that back-propagating through contact and long trajectories drastically reduces gradient accuracy. Using this insight, we propose AHAC to dynamically adapt its roll-out horizon to avoid differentiating through stiff contact. AHAC is a first-order model-based RL algorithm that learns high-dimensional tasks in minutes (wall clock) and outperforms PPO by 40%, even in the limit of data provided to PPO. This work is led by Ignat Georgiev alongside Krishnan Srinivasan, Jie Xu, Eric Heiden and ample assistance from warp team at NVIDIA Robotics (Miles Macklin)

Animesh Garg

52,308 次观看 • 2 年前

RL is painfully slow 😭 — bottlenecked by super-long CoT rollout. 🔭 Sparse attention should help, but naive sparse rollout hits a brutal efficiency–stability tradeoff: A tedious trial-and-error sparsity sweep for each dense policy is required before an actual RL run. 🐤Sparrow chirps no more pain! Introduce Sparrow: Sparse Rollout for stable and efficient long-context RL. Sparrow finds that: 💡As long as we keep the tail distribution mismatch throughout the sparse rollout above a critical threshold, the RL training will be stable. 💡Even cooler! Through comprehensive control studies of Qwen3-1.7B, 4B, 8B thinking models RL with 40K rollout max length, the critical threshold stays constant across model sizes. 💡Sparrow then finds the optimal dynamic sparse schedule to reach the threshold with minimal cost. 💡Sparrow's findings are empirically validated to generalize in Qwen3-14B, and hold on both Math and Coding RL. 🐤Sparrow empirically helps achieve 2.2× / 2.4× / 2.0× rollout speedup on Qwen3 1.7B / 4B / 8B thinking models, while keeping training stability over extended RL steps. We release the 🐤bird in the following formats. [1/n] Paper: Code: Blog:

RL is painfully slow 😭 — bottlenecked by super-long CoT rollout. 🔭 Sparse attention should help, but naive sparse rollout hits a brutal efficiency–stability tradeoff: A tedious trial-and-error sparsity sweep for each dense policy is required before an actual RL run. 🐤Sparrow chirps no more pain! Introduce Sparrow: Sparse Rollout for stable and efficient long-context RL. Sparrow finds that: 💡As long as we keep the tail distribution mismatch throughout the sparse rollout above a critical threshold, the RL training will be stable. 💡Even cooler! Through comprehensive control studies of Qwen3-1.7B, 4B, 8B thinking models RL with 40K rollout max length, the critical threshold stays constant across model sizes. 💡Sparrow then finds the optimal dynamic sparse schedule to reach the threshold with minimal cost. 💡Sparrow's findings are empirically validated to generalize in Qwen3-14B, and hold on both Math and Coding RL. 🐤Sparrow empirically helps achieve 2.2× / 2.4× / 2.0× rollout speedup on Qwen3 1.7B / 4B / 8B thinking models, while keeping training stability over extended RL steps. We release the 🐤bird in the following formats. [1/n] Paper: Code: Blog:

Infini-AI-Lab

78,644 次观看 • 2 个月前

A Letter to Our Community: The Road Ahead for Robotics To our Community and Partners, As we step into 2026, our mission at Axis is clearer than ever: Constructing the definitive End-to-End Scaling Layer for Robotics. Our goal is to accelerate the transfer of diverse human intelligence into Robotics General Intelligence (RGI). By owning the critical path of intelligence creation, we are turning the physical limitations of robotics into a scalable, software-driven future. Here is our strategic outlook and roadmap for the year ahead. The Core Thesis: Simulation is the Only Way Out The path to RGI is currently blocked by Data Scarcity, Generalization Fragility, and Hardware Fragmentation. At Axis, we believe Simulation is the only way out. Our Simulation Data Platform and Data Augmentation Engine transform raw data into "Synthetic Gold". Backed by academic milestones like Roboverse, Skill Blending, and GraspVLA, we have proven that pure simulation can achieve the generalization required for the real world. We don’t just collect data; we architect it. The Engine: Why Crypto? We believe RGI should come from all, not a few. Crypto is not just a feature; it is the primitive that powers our entire ecosystem flywheel: - Incentive Mechanism: Democratizing contribution and rewarding the trainers and developers. - Assetization: Turning proprietary data and refined models into liquid, ownable assets. - Verifiable Workflow: We are opening the "Black Box" of AI. By bringing total transparency to the Task Generation → Data Collection → Model Training pipeline, we ensure every byte of intelligence is verifiable, traceable, and secure. 2026 Strategic Deliverables This year, we are committed to delivering three foundational pillars: - The World's Largest Training Dataset for Robots: A robot training set—diverse, high-quality interaction data at an unprecedented scale. - A Robotics Foundation Model: A universal robotic brain trained on our pure simulation and synthetic data, capable of robust cross-embodiment transfer and open-world adaptability. - Evolvable Robot Hardware: Robots deployed with Axis models that autonomously evolve through continuous interaction, turning every deployment into a self-improving node within our RGI network. The Ultimate Vision We are building more than models; we are architecting the Distributed Machine Economy. A future where every dataset, model, and robotic embodiment is a verifiable asset in a global, autonomous network. Thank you for building the future of intelligence with us✌️📷

A Letter to Our Community: The Road Ahead for Robotics To our Community and Partners, As we step into 2026, our mission at Axis is clearer than ever: Constructing the definitive End-to-End Scaling Layer for Robotics. Our goal is to accelerate the transfer of diverse human intelligence into Robotics General Intelligence (RGI). By owning the critical path of intelligence creation, we are turning the physical limitations of robotics into a scalable, software-driven future. Here is our strategic outlook and roadmap for the year ahead. The Core Thesis: Simulation is the Only Way Out The path to RGI is currently blocked by Data Scarcity, Generalization Fragility, and Hardware Fragmentation. At Axis, we believe Simulation is the only way out. Our Simulation Data Platform and Data Augmentation Engine transform raw data into "Synthetic Gold". Backed by academic milestones like Roboverse, Skill Blending, and GraspVLA, we have proven that pure simulation can achieve the generalization required for the real world. We don’t just collect data; we architect it. The Engine: Why Crypto? We believe RGI should come from all, not a few. Crypto is not just a feature; it is the primitive that powers our entire ecosystem flywheel: - Incentive Mechanism: Democratizing contribution and rewarding the trainers and developers. - Assetization: Turning proprietary data and refined models into liquid, ownable assets. - Verifiable Workflow: We are opening the "Black Box" of AI. By bringing total transparency to the Task Generation → Data Collection → Model Training pipeline, we ensure every byte of intelligence is verifiable, traceable, and secure. 2026 Strategic Deliverables This year, we are committed to delivering three foundational pillars: - The World's Largest Training Dataset for Robots: A robot training set—diverse, high-quality interaction data at an unprecedented scale. - A Robotics Foundation Model: A universal robotic brain trained on our pure simulation and synthetic data, capable of robust cross-embodiment transfer and open-world adaptability. - Evolvable Robot Hardware: Robots deployed with Axis models that autonomously evolve through continuous interaction, turning every deployment into a self-improving node within our RGI network. The Ultimate Vision We are building more than models; we are architecting the Distributed Machine Economy. A future where every dataset, model, and robotic embodiment is a verifiable asset in a global, autonomous network. Thank you for building the future of intelligence with us✌️📷

Axis Robotics

27,858 次观看 • 7 个月前

🚨 New Content Alert 🚨 Sneak peek of our Twist Pickup Series with Tyler Biadasz 🔥 In the trenches, where games are won or lost, it’s all about preparation 🏈. As defenses evolve, offensive linemen need a solid approach to handle twists & stunts. That’s why we created The 6 Keys to Twist Pickup—your guide to mastering fundamentals & handling anything defenses throw at you 🦾 Let’s break it down and level up your game. Only on the OL Masterminds Training App 📲 🔗LINK IN BIO🔗

🚨 New Content Alert 🚨 Sneak peek of our Twist Pickup Series with Tyler Biadasz 🔥 In the trenches, where games are won or lost, it’s all about preparation 🏈. As defenses evolve, offensive linemen need a solid approach to handle twists & stunts. That’s why we created The 6 Keys to Twist Pickup—your guide to mastering fundamentals & handling anything defenses throw at you 🦾 Let’s break it down and level up your game. Only on the OL Masterminds Training App 📲 🔗LINK IN BIO🔗

OL Masterminds Training

13,312 次观看 • 1 年前

🔥🚨DEVELOPING: Moviegoers have started complaining to studios about casting Jared Leto despite thousands of Hollywood fans claiming they dislike him in all of the major movies he has been cast in after the new He-Man “Masters Of The Universe” trailer gave a more detailed look into Leto’s character. Fan: “Why on earth does Jared Leto keep getting huge roles? Pretty widley disliked by the public, consistently involved in huge box office bombs, never in awards talk anymore. Someone explain.”

🔥🚨DEVELOPING: Moviegoers have started complaining to studios about casting Jared Leto despite thousands of Hollywood fans claiming they dislike him in all of the major movies he has been cast in after the new He-Man “Masters Of The Universe” trailer gave a more detailed look into Leto’s character. Fan: “Why on earth does Jared Leto keep getting huge roles? Pretty widley disliked by the public, consistently involved in huge box office bombs, never in awards talk anymore. Someone explain.”

Dom Lucre | Breaker of Narratives

255,720 次观看 • 4 个月前

🇺🇸 Trump: "We didn't start this war. Iran has been at war with us for a long time." 🇺🇸Caroline: "How many US citizens have they attacked on US soil?" It's Zero 🔥 Also, why are our soldiers in the Middle East? Its Because of Oil. 🔥 Caroline has single handedly exposed Trump’s propaganda. What a brave lady 👏

🇺🇸 Trump: "We didn't start this war. Iran has been at war with us for a long time." 🇺🇸Caroline: "How many US citizens have they attacked on US soil?" It's Zero 🔥 Also, why are our soldiers in the Middle East? Its Because of Oil. 🔥 Caroline has single handedly exposed Trump’s propaganda. What a brave lady 👏

InfoGram

346,069 次观看 • 22 天前

This work makes a humanoid robot do simple parkour moves by looking with a depth camera and choosing the right move on the fly. The big deal is that it turns lots of small human moves into long, real-time robot behavior, without hand-coding every transition or retraining for each new course. A humanoid robot is usually good at steady walking, but it often fails when it has to do fast moves like jumping up, vaulting, or rolling, and then keep going to the next obstacle. The hard part is that you cannot easily collect training data for every possible obstacle shape, distance, and mistake, so robots end up learning a few moves that only work in a narrow setup. This work starts from short clips of real human parkour moves, like stepping over, vaulting, climbing, and rolling. It uses motion matching, which is basically a smart “pick the next clip that fits best right now” search, to stitch those short clips into a long, smooth plan that looks like a human doing a whole course. Then it trains a controller with reinforcement learning (RL), which means the robot learns by trial and error to copy that plan while staying balanced and not falling. After training separate expert controllers for different moves, it compresses them into 1 controller that uses only onboard depth sensing and a simple “go this fast in this direction” command. In real tests on a Unitree G1 humanoid, it can clear multiple obstacles in a row, adapt when obstacles get moved, and climb a wall up to 1.25m.

This work makes a humanoid robot do simple parkour moves by looking with a depth camera and choosing the right move on the fly. The big deal is that it turns lots of small human moves into long, real-time robot behavior, without hand-coding every transition or retraining for each new course. A humanoid robot is usually good at steady walking, but it often fails when it has to do fast moves like jumping up, vaulting, or rolling, and then keep going to the next obstacle. The hard part is that you cannot easily collect training data for every possible obstacle shape, distance, and mistake, so robots end up learning a few moves that only work in a narrow setup. This work starts from short clips of real human parkour moves, like stepping over, vaulting, climbing, and rolling. It uses motion matching, which is basically a smart “pick the next clip that fits best right now” search, to stitch those short clips into a long, smooth plan that looks like a human doing a whole course. Then it trains a controller with reinforcement learning (RL), which means the robot learns by trial and error to copy that plan while staying balanced and not falling. After training separate expert controllers for different moves, it compresses them into 1 controller that uses only onboard depth sensing and a simple “go this fast in this direction” command. In real tests on a Unitree G1 humanoid, it can clear multiple obstacles in a row, adapt when obstacles get moved, and climb a wall up to 1.25m.

Rohan Paul

37,121 次观看 • 5 个月前

Why the Boeing 787 Dreamliner Keeps Peeling Paint — Despite Being a Favorite for Airlines. The 787’s paint issue isn’t cosmetic—it’s structural. Its wings are built in layers: carbon fiber (structure), copper mesh (lightning protection), and a thin fiberglass layer on top. The paint sits on that outer layer. Over time, UV exposure degrades the fiberglass into powder. That powder lifts off with the ultra-thin paint, exposing the underlying layers. Unlike older jets, the 787’s paint system is part of its lightning protection design—so airlines can’t just apply thicker paint or use different primers without certification changes. Airlines still love the 787 for its efficiency—but this has quietly become one of its most persistent technical headaches. Video from LATAM Dreamliner 2024. 📸: u/eem29

Why the Boeing 787 Dreamliner Keeps Peeling Paint — Despite Being a Favorite for Airlines. The 787’s paint issue isn’t cosmetic—it’s structural. Its wings are built in layers: carbon fiber (structure), copper mesh (lightning protection), and a thin fiberglass layer on top. The paint sits on that outer layer. Over time, UV exposure degrades the fiberglass into powder. That powder lifts off with the ultra-thin paint, exposing the underlying layers. Unlike older jets, the 787’s paint system is part of its lightning protection design—so airlines can’t just apply thicker paint or use different primers without certification changes. Airlines still love the 787 for its efficiency—but this has quietly become one of its most persistent technical headaches. Video from LATAM Dreamliner 2024. 📸: u/eem29

Turbine Traveller

117,399 次观看 • 4 个月前

Sewage discharging in #LakeDistrict river. United Utilities doing what they do best. Video taken at 10am this morning on the river Brathay. The Environment Agency is permitting up to 1.1 million litres a day of sewage to come out of this pipe. This is making its way into Englands largest lake in a UNESCO world heritage site and National Park. Save Windermere has made a new short film about this pipe ⬇️ Help the campaign by signing up to our website ⬇️ Feargal Sharkey Prof Jamie Woodward Windrush WASP

Sewage discharging in #LakeDistrict river. United Utilities doing what they do best. Video taken at 10am this morning on the river Brathay. The Environment Agency is permitting up to 1.1 million litres a day of sewage to come out of this pipe. This is making its way into Englands largest lake in a UNESCO world heritage site and National Park. Save Windermere has made a new short film about this pipe ⬇️ Help the campaign by signing up to our website ⬇️ Feargal Sharkey Prof Jamie Woodward Windrush WASP

MattStaniek

239,795 次观看 • 2 年前

We’re excited to introduce ShinkaEvolve: An open-source framework that evolves programs for scientific discovery with unprecedented sample-efficiency. Blog: Code: Like AlphaEvolve and its variants, our framework leverages LLMs to find state-of-the-art solutions to complex problems, but using orders of magnitude fewer resources! Many evolutionary AI systems are powerful but act like brute-force engines, burning thousands of samples to find good solutions. This makes discovery slow and expensive. We took inspiration from the efficiency of nature. ‘Shinka’ (進化) is Japanese for evolution, and we designed our system to be just as resourceful. On the classic circle packing optimization problem, ShinkaEvolve discovered a new state-of-the-art solution using only 150 samples. This is a big leap in efficiency compared to previous methods that required thousands of evaluations. We applied ShinkaEvolve to a diverse set of hard problems with real-world applications: 1/ AIME Math Reasoning: It evolved sophisticated agentic scaffolds that significantly outperform strong baselines, discovering an entire Pareto frontier of solutions trading performance for efficiency. 2/ Competitive Programming: On ALE-Bench (a benchmark for NP-Hard optimization problems), ShinkaEvolve took the best existing agent's solutions and improved them, turning a 5th place solution on one task into a 2nd place leaderboard rank in a competitive programming competition. 3/ LLM Training: We even turned ShinkaEvolve inward to improve LLMs themselves. It tackled the open challenge of designing load balancing losses for Mixture-of-Experts (MoE) models. It discovered a novel loss function that leads to better expert specialization and consistently improves model performance and perplexity. ShinkaEvolve achieves its remarkable sample-efficiency through three key innovations that work together: (1) an adaptive parent sampling strategy to balance exploration and exploitation, (2) novelty-based rejection filtering to avoid redundant work, and (3) a bandit-based LLM ensemble that dynamically picks the best model for the job. By making ShinkaEvolve open-source and highly sample-efficient, our goal is to democratize access to advanced, open-ended discovery tools. Our vision for ShinkaEvolve is to be an easy-to-use companion tool to help scientists and engineers with their daily work. We believe that building more efficient, nature-inspired systems is key to unlocking the future of AI-driven scientific research. We are excited to see what the community builds with it! Learn more in our technical report:

We’re excited to introduce ShinkaEvolve: An open-source framework that evolves programs for scientific discovery with unprecedented sample-efficiency. Blog: Code: Like AlphaEvolve and its variants, our framework leverages LLMs to find state-of-the-art solutions to complex problems, but using orders of magnitude fewer resources! Many evolutionary AI systems are powerful but act like brute-force engines, burning thousands of samples to find good solutions. This makes discovery slow and expensive. We took inspiration from the efficiency of nature. ‘Shinka’ (進化) is Japanese for evolution, and we designed our system to be just as resourceful. On the classic circle packing optimization problem, ShinkaEvolve discovered a new state-of-the-art solution using only 150 samples. This is a big leap in efficiency compared to previous methods that required thousands of evaluations. We applied ShinkaEvolve to a diverse set of hard problems with real-world applications: 1/ AIME Math Reasoning: It evolved sophisticated agentic scaffolds that significantly outperform strong baselines, discovering an entire Pareto frontier of solutions trading performance for efficiency. 2/ Competitive Programming: On ALE-Bench (a benchmark for NP-Hard optimization problems), ShinkaEvolve took the best existing agent's solutions and improved them, turning a 5th place solution on one task into a 2nd place leaderboard rank in a competitive programming competition. 3/ LLM Training: We even turned ShinkaEvolve inward to improve LLMs themselves. It tackled the open challenge of designing load balancing losses for Mixture-of-Experts (MoE) models. It discovered a novel loss function that leads to better expert specialization and consistently improves model performance and perplexity. ShinkaEvolve achieves its remarkable sample-efficiency through three key innovations that work together: (1) an adaptive parent sampling strategy to balance exploration and exploitation, (2) novelty-based rejection filtering to avoid redundant work, and (3) a bandit-based LLM ensemble that dynamically picks the best model for the job. By making ShinkaEvolve open-source and highly sample-efficient, our goal is to democratize access to advanced, open-ended discovery tools. Our vision for ShinkaEvolve is to be an easy-to-use companion tool to help scientists and engineers with their daily work. We believe that building more efficient, nature-inspired systems is key to unlocking the future of AI-driven scientific research. We are excited to see what the community builds with it! Learn more in our technical report:

Sakana AI

360,234 次观看 • 10 个月前

Homa is excited to announce that Valentine is now a playable character in Aquarium Land, one of our most popular games. Creating a successful IP is challenging, as it requires gaining mass-market attention. This involves hundreds of millions of people acknowledging a character, following its journey, and becoming part of its community. To achieve this, Homa is investing significant resources in promoting Valentine through comics, illustrations, animations, stories, and games. By integrating Valentine into Aquarium Land, we take the first step in bridging awareness between Web2 and Web3. We aim to onboard millions of users to take part in Valentine's journey, play as her, build an emotional connection with the character, and read her stories. Aquarium Land has been downloaded more than 25 million times worldwide and has over 300,000 daily active players. As a Homa Holder, you are part of our ambitious vision: making Valentine a character that will make history by becoming part of the culture. Join us on our journey to go beyond Web3 and onboard millions of players and fans across the globe.

Homa is excited to announce that Valentine is now a playable character in Aquarium Land, one of our most popular games. Creating a successful IP is challenging, as it requires gaining mass-market attention. This involves hundreds of millions of people acknowledging a character, following its journey, and becoming part of its community. To achieve this, Homa is investing significant resources in promoting Valentine through comics, illustrations, animations, stories, and games. By integrating Valentine into Aquarium Land, we take the first step in bridging awareness between Web2 and Web3. We aim to onboard millions of users to take part in Valentine's journey, play as her, build an emotional connection with the character, and read her stories. Aquarium Land has been downloaded more than 25 million times worldwide and has over 300,000 daily active players. As a Homa Holder, you are part of our ambitious vision: making Valentine a character that will make history by becoming part of the culture. Join us on our journey to go beyond Web3 and onboard millions of players and fans across the globe.

homa

19,927 次观看 • 3 年前

If the Universe’s only way to realize itself is through intelligent beings, why did it not protect life during earth’s evolution? Or does it have other purposes — and are we merely a byproduct of its creation? : If the Universe can only know itself through conscious minds, why did it let life on Earth teeter on the edge of extinction dozens of times? Asteroids, ice ages, supervolcanoes, and mass die-offs all struck without mercy. No shield, no mercy, no intervention. That silence is the answer. The Universe is not a guardian. It is an arena. It does not care whether intelligence ever arises, survives, or dies. The emergence of minds is not its goal; it is an accidental side effect of laws that permit complexity. We are not protected children of a loving cosmos. We are the rare, fragile spark that sometimes flickers into being inside an indifferent storm. Our existence proves nothing about cosmic purpose. It only proves that, out of countless dead rocks, one managed to keep the spark alive long enough to ask why. The Universe has no obligation to us. We have an obligation to the spark. That is the whole story.

If the Universe’s only way to realize itself is through intelligent beings, why did it not protect life during earth’s evolution? Or does it have other purposes — and are we merely a byproduct of its creation? : If the Universe can only know itself through conscious minds, why did it let life on Earth teeter on the edge of extinction dozens of times? Asteroids, ice ages, supervolcanoes, and mass die-offs all struck without mercy. No shield, no mercy, no intervention. That silence is the answer. The Universe is not a guardian. It is an arena. It does not care whether intelligence ever arises, survives, or dies. The emergence of minds is not its goal; it is an accidental side effect of laws that permit complexity. We are not protected children of a loving cosmos. We are the rare, fragile spark that sometimes flickers into being inside an indifferent storm. Our existence proves nothing about cosmic purpose. It only proves that, out of countless dead rocks, one managed to keep the spark alive long enough to ask why. The Universe has no obligation to us. We have an obligation to the spark. That is the whole story.

Zafar Mirzo | Quotes

272,822 次观看 • 8 个月前

I’m grateful to our warriors for their precision – today, Ukraine’s long-range sanctions have once again reached Perm, which is more than 1,500 kilometers from our border. Recently, there have also been important results in Chelyabinsk – up to 1,800 kilometers away, as well as in Yekaterinburg – nearly 2,000 kilometers away. The effects of Ukraine’s long-range capabilities have also been felt in Novorossiysk, Krymsk, Tuapse, and in the Samara and Nizhny Novgorod regions. We have repeatedly offered the Russian leadership the option of moving toward peace. In response, we have received only new Russian strikes. That is exactly why Ukraine’s long-range sanctions are extending to distant locations in Russia linked to its military-industrial complex, war infrastructure, and the financing of its aggression. Every day, Russia can make a choice and end its war. And not for a few hours in order to receive our permission to hold a parade in Moscow, but in a way that protects human lives. People must be valued, not parades. There is a need to establish peace, rather than running around the world’s capitals begging for a pause on May 9. We need peace. Thank you to everyone helping bring Russia to this realization.

I’m grateful to our warriors for their precision – today, Ukraine’s long-range sanctions have once again reached Perm, which is more than 1,500 kilometers from our border. Recently, there have also been important results in Chelyabinsk – up to 1,800 kilometers away, as well as in Yekaterinburg – nearly 2,000 kilometers away. The effects of Ukraine’s long-range capabilities have also been felt in Novorossiysk, Krymsk, Tuapse, and in the Samara and Nizhny Novgorod regions. We have repeatedly offered the Russian leadership the option of moving toward peace. In response, we have received only new Russian strikes. That is exactly why Ukraine’s long-range sanctions are extending to distant locations in Russia linked to its military-industrial complex, war infrastructure, and the financing of its aggression. Every day, Russia can make a choice and end its war. And not for a few hours in order to receive our permission to hold a parade in Moscow, but in a way that protects human lives. People must be valued, not parades. There is a need to establish peace, rather than running around the world’s capitals begging for a pause on May 9. We need peace. Thank you to everyone helping bring Russia to this realization.

Volodymyr Zelenskyy / Володимир Зеленський

233,452 次观看 • 3 个月前

It's 2030 and you are reviewing humanoid robots. A Tesla. A Google. An Apple. An OpenAI. A Meta. A Figure. And a bunch of Chinese-made ones. Which one is best, and why? I think the Tesla understands the world much better. Why? There were eight Teslas around me on the freeway today. Start there. No other robot company has that data. But my robot is parked at the local high school twice a day. Its cameras see humans in all of our weirdness. How we move. Where we go. Where we walk. Who we talk with. What you are wearing. Whether your hair was combed this morning. That data will lead to robotics breakthroughs. Apple might keep up with its Vision Pro data, but it is too freaked out by the privacy implications of using said data. (On the front are six cameras and a couple of TOF -- Time Of Flight -- sensors that can see everything in your home in great detail). Google has a lot of data, for sure. All my: 1. Email. 2. Calendars. 3. Photos. 4. TV watching behavior. 5. Contacts. 6. Documents and spreadsheets. 7. Files. 8. Location data. So I expect Google's robot will be attractive to many. But how do you see the others shake out over the next five years? Make some guesses. But remember what an AI pioneer told me years ago about AI: it's all about the data. The Chinese ones have huge advantages: the Chinese have more data on their citizens, and many more citizens to boot AND they can make robots cheaper than we can. But now that you know OpenAI is building its own robot you have caught wind of what I've heard from many in San Francisco and Silicon Valley: that humanoid robots are the real prize of AI and will be highly profitable for those that can make them and find customers willing to buy them. Here, too, I learned long ago never to bet against Elon Musk. Will you?

It's 2030 and you are reviewing humanoid robots. A Tesla. A Google. An Apple. An OpenAI. A Meta. A Figure. And a bunch of Chinese-made ones. Which one is best, and why? I think the Tesla understands the world much better. Why? There were eight Teslas around me on the freeway today. Start there. No other robot company has that data. But my robot is parked at the local high school twice a day. Its cameras see humans in all of our weirdness. How we move. Where we go. Where we walk. Who we talk with. What you are wearing. Whether your hair was combed this morning. That data will lead to robotics breakthroughs. Apple might keep up with its Vision Pro data, but it is too freaked out by the privacy implications of using said data. (On the front are six cameras and a couple of TOF -- Time Of Flight -- sensors that can see everything in your home in great detail). Google has a lot of data, for sure. All my: 1. Email. 2. Calendars. 3. Photos. 4. TV watching behavior. 5. Contacts. 6. Documents and spreadsheets. 7. Files. 8. Location data. So I expect Google's robot will be attractive to many. But how do you see the others shake out over the next five years? Make some guesses. But remember what an AI pioneer told me years ago about AI: it's all about the data. The Chinese ones have huge advantages: the Chinese have more data on their citizens, and many more citizens to boot AND they can make robots cheaper than we can. But now that you know OpenAI is building its own robot you have caught wind of what I've heard from many in San Francisco and Silicon Valley: that humanoid robots are the real prize of AI and will be highly profitable for those that can make them and find customers willing to buy them. Here, too, I learned long ago never to bet against Elon Musk. Will you?

Robert Scoble

33,804 次观看 • 1 年前

What does market conviction look like? A $350M raise at a $7.5B valuation. Today, we have closed that $350M round and nearly doubled our valuation, all backed by Vitruvian Partners, Accenture Ventures, J.P. Morgan Growth Equity Partners, D. E. Shaw Ventures, Pinegrove Opportunity Partners, and our returning investors CapitalG, Goldman Sachs Alternatives, and Viking Global Investors. Doubling down on their investment, we have also established a new channel partnership with Accenture to further fuel organizations embedding and scaling market intelligence into their core agentic workflows. The era of fragmented tools in market intelligence has given way to a more integrated approach with the advent of AI and that's why we are also announcing the launch of SuperAnalyst. AI prompts and queries are the workflow most analysts know today but now multi-step research and monitoring tasks can be automated on the user's behalf. These announcements are more than just momentous milestones: they are an inflection point for the future of market intelligence. We couldn't be more excited to keep building it.

What does market conviction look like? A $350M raise at a $7.5B valuation. Today, we have closed that $350M round and nearly doubled our valuation, all backed by Vitruvian Partners, Accenture Ventures, J.P. Morgan Growth Equity Partners, D. E. Shaw Ventures, Pinegrove Opportunity Partners, and our returning investors CapitalG, Goldman Sachs Alternatives, and Viking Global Investors. Doubling down on their investment, we have also established a new channel partnership with Accenture to further fuel organizations embedding and scaling market intelligence into their core agentic workflows. The era of fragmented tools in market intelligence has given way to a more integrated approach with the advent of AI and that's why we are also announcing the launch of SuperAnalyst. AI prompts and queries are the workflow most analysts know today but now multi-step research and monitoring tasks can be automated on the user's behalf. These announcements are more than just momentous milestones: they are an inflection point for the future of market intelligence. We couldn't be more excited to keep building it.

AlphaSense

36,295 次观看 • 2 个月前

LongWriter Unleashing 10,000+ Word Generation from Long Context LLMs discuss: Current long context large language models (LLMs) can process inputs up to 100,000 tokens, yet struggle to generate outputs exceeding even a modest length of 2,000 words. Through controlled experiments, we find that the model's effective generation length is inherently bounded by the sample it has seen during supervised fine-tuning (SFT). In other words, their output limitation is due to the scarcity of long-output examples in existing SFT datasets. To address this, we introduce AgentWrite, an agent-based pipeline that decomposes ultra-long generation tasks into subtasks, enabling off-the-shelf LLMs to generate coherent outputs exceeding 20,000 words. Leveraging AgentWrite, we construct LongWriter-6k, a dataset containing 6,000 SFT data with output lengths ranging from 2k to 32k words. By incorporating this dataset into model training, we successfully scale the output length of existing models to over 10,000 words while maintaining output quality. We also develop LongBench-Write, a comprehensive benchmark for evaluating ultra-long generation capabilities. Our 9B parameter model, further improved through DPO, achieves state-of-the-art performance on this benchmark, surpassing even much larger proprietary models. In general, our work demonstrates that existing long context LLM already possesses the potential for a larger output window--all you need is data with extended output during model alignment to unlock this capability.

LongWriter Unleashing 10,000+ Word Generation from Long Context LLMs discuss: Current long context large language models (LLMs) can process inputs up to 100,000 tokens, yet struggle to generate outputs exceeding even a modest length of 2,000 words. Through controlled experiments, we find that the model's effective generation length is inherently bounded by the sample it has seen during supervised fine-tuning (SFT). In other words, their output limitation is due to the scarcity of long-output examples in existing SFT datasets. To address this, we introduce AgentWrite, an agent-based pipeline that decomposes ultra-long generation tasks into subtasks, enabling off-the-shelf LLMs to generate coherent outputs exceeding 20,000 words. Leveraging AgentWrite, we construct LongWriter-6k, a dataset containing 6,000 SFT data with output lengths ranging from 2k to 32k words. By incorporating this dataset into model training, we successfully scale the output length of existing models to over 10,000 words while maintaining output quality. We also develop LongBench-Write, a comprehensive benchmark for evaluating ultra-long generation capabilities. Our 9B parameter model, further improved through DPO, achieves state-of-the-art performance on this benchmark, surpassing even much larger proprietary models. In general, our work demonstrates that existing long context LLM already possesses the potential for a larger output window--all you need is data with extended output during model alignment to unlock this capability.

AK

50,995 次观看 • 2 年前

My home was a magical place. Only ashes remain. The outpouring of support from community has been monumental and every message, donation, gesture has truly been food for me these last few days. We’ve got a long road ahead of us. We evacuated with only the clothes on our backs so we truly have nothing left. My art, my jewelry, my clothing, my books, my photos - everything curated and collected in my material in my life is now gone, including the home we had poured everything into to buy in the first place. We are underinsured, it will be a battle. Sobering. Hard. Life-Changing. But… I want everyone to remember its beauty and optimism, the joy the disco cabin brought me and so many others who gathered here. At this exact time last year - Punks came together at my home as the sunset kissed the ocean in a sky of pink, sweet tunes drifting over us with tacos in hand. And perhaps there is also something poetic in this total loss, a silver lining I will find one day? This moment was framed to me as the beginning of a new “hero’s journey” and being a lover of Joseph Campbell - and that concept feels true. 🐦‍🔥 “We're in a freefall into future. We don't know where we're going. Things are changing so fast, and always when you're going through a long tunnel, anxiety comes along. And all you have to do to transform your hell into a paradise is to turn your fall into a voluntary act. It's a very interesting shift of perspective and that's all it is... joyful participation in the sorrows and everything changes.”

My home was a magical place. Only ashes remain. The outpouring of support from community has been monumental and every message, donation, gesture has truly been food for me these last few days. We’ve got a long road ahead of us. We evacuated with only the clothes on our backs so we truly have nothing left. My art, my jewelry, my clothing, my books, my photos - everything curated and collected in my material in my life is now gone, including the home we had poured everything into to buy in the first place. We are underinsured, it will be a battle. Sobering. Hard. Life-Changing. But… I want everyone to remember its beauty and optimism, the joy the disco cabin brought me and so many others who gathered here. At this exact time last year - Punks came together at my home as the sunset kissed the ocean in a sky of pink, sweet tunes drifting over us with tacos in hand. And perhaps there is also something poetic in this total loss, a silver lining I will find one day? This moment was framed to me as the beginning of a new “hero’s journey” and being a lover of Joseph Campbell - and that concept feels true. 🐦‍🔥 “We're in a freefall into future. We don't know where we're going. Things are changing so fast, and always when you're going through a long tunnel, anxiety comes along. And all you have to do to transform your hell into a paradise is to turn your fall into a voluntary act. It's a very interesting shift of perspective and that's all it is... joyful participation in the sorrows and everything changes.”

STONE

573,828 次观看 • 1 年前

🚨 Obvi’s influencer program didn’t just scale — it became a full-blown moat. Want to know how we did it? We built a system where 100+ brand ambassadors and 30+ long-term creator partners were posting about us daily. ❌ No upfront payments. ❌ No flaky one-offs. ❌ Just a steady stream of UGC that actually moved product. Most brands gamble on influencer marketing. They pay big upfront fees, only get “one-and-done” posts, and develop long-term leverage or longevity. But we took a different route. Instead of chasing creators, we built our own “influencer Illuminati” — a structured program that: - Starts with smart product seeding (low risk, high signal) - Converts winners into performance-based ambassadors - Elevates top performers into compounding, long-term partnerships That’s why our influencer program is one of our most consistent and cost-effective growth engines. I broke it all down in a recent presentation with Yash Chavan ⚡️ — frameworks, real tactics, and the painful lessons we learned the hard way. Want the deck? Drop “ILLUMINATI” in the comments, and I’ll send it your way. 👇🏽

Ash

33,025 次观看 • 1 年前

𝗘𝘃𝗲𝗿𝘆𝗼𝗻𝗲’𝘀 𝘁𝗮𝗹𝗸𝗶𝗻𝗴 𝗮𝗯𝗼𝘂𝘁 “𝗣𝗵𝘆𝘀𝗶𝗰𝗮𝗹 𝗔𝗜" - the idea that we can simulate real-world environments so well that robots trained in simulation will work perfectly in reality. 𝗧𝗵𝗲 𝗽𝗿𝗼𝗺𝗶𝘀𝗲: Train in virtual worlds → deploy anywhere. 𝗧𝗵𝗲 𝗿𝗲𝗮𝗹𝗶𝘁𝘆: I’ve seen too many teams fall into this trap. After working with manipulation teams at Berkeley, Imperial, and Dyson, here’s the pattern: • 𝗪𝗲𝗲𝗸 𝟭: “Our policy works perfectly in simulation!” • 𝗪𝗲𝗲𝗸 𝟰: “Why doesn’t this work on real objects?” • 𝗠𝗼𝗻𝘁𝗵 𝟮: “We basically need to retrain from scratch with real data.” 𝗧𝗵𝗲 𝗴𝗮𝗽 𝘀𝗶𝗺𝘂𝗹𝗮𝘁𝗶𝗼𝗻𝘀 𝗰𝗮𝗻’𝘁 𝗯𝗿𝗶𝗱𝗴𝗲: Unlike blind locomotion policies that can get away with sim-to-real transfer because they rely mainly on proprioception and contact forces, 𝘃𝗶𝘀𝗶𝗼𝗻-𝗴𝘂𝗶𝗱𝗲𝗱 𝗺𝗮𝗻𝗶𝗽𝘂𝗹𝗮𝘁𝗶𝗼𝗻 𝗶𝘀 𝗲𝘅𝘁𝗿𝗲𝗺𝗲𝗹𝘆 𝘀𝗲𝗻𝘀𝗶𝘁𝗶𝘃𝗲 𝘁𝗼 𝘃𝗶𝘀𝘂𝗮𝗹 𝗱𝗼𝗺𝗮𝗶𝗻 𝗴𝗮𝗽𝘀. • Real friction vs simulated surface textures • Manufacturing tolerances vs perfect CAD models • Dynamic lighting vs controlled virtual environments • Sensor noise vs instantaneous virtual readings 𝗛𝗲𝗿𝗲'𝘀 𝘄𝗵𝗮𝘁 𝗽𝗲𝗼𝗽𝗹𝗲 𝗱𝗼𝗻'𝘁 𝘁𝗮𝗹𝗸 𝗮𝗯𝗼𝘂𝘁: Building these detailed simulated environments takes forever. If it takes 7 days to build a simulated kitchen in simulation, wouldn't it be better to just collect real-world data in a real kitchen instead? 𝗗𝗼𝗻'𝘁 𝗴𝗲𝘁 𝗺𝗲 𝘄𝗿𝗼𝗻𝗴 - simulation is incredible for debugging, safety testing, and exploring edge cases. But it's not a magic solution to real-world deployment. 𝗪𝗵𝗮𝘁 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝘄𝗼𝗿𝗸𝘀: Use simulation strategically while making real-world data collection as efficient and flexible as possible. This is why Neuracore focuses on streamlined real-world data infrastructure. Because no amount of virtual training can replace understanding how your robot actually behaves in actual environments. 𝗧𝗵𝗲 𝗽𝗵𝘆𝘀𝗶𝗰𝘀 𝗼𝗳 𝘆𝗼𝘂𝗿 𝗱𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁 𝗲𝗻𝘃𝗶𝗿𝗼𝗻𝗺𝗲𝗻𝘁 𝗰𝗮𝗻'𝘁 𝗯𝗲 𝘀𝗶𝗺𝘂𝗹𝗮𝘁𝗲𝗱 𝗮𝘄𝗮𝘆. What’s been your experience with sim-to-real transfer?

𝗘𝘃𝗲𝗿𝘆𝗼𝗻𝗲’𝘀 𝘁𝗮𝗹𝗸𝗶𝗻𝗴 𝗮𝗯𝗼𝘂𝘁 “𝗣𝗵𝘆𝘀𝗶𝗰𝗮𝗹 𝗔𝗜" - the idea that we can simulate real-world environments so well that robots trained in simulation will work perfectly in reality. 𝗧𝗵𝗲 𝗽𝗿𝗼𝗺𝗶𝘀𝗲: Train in virtual worlds → deploy anywhere. 𝗧𝗵𝗲 𝗿𝗲𝗮𝗹𝗶𝘁𝘆: I’ve seen too many teams fall into this trap. After working with manipulation teams at Berkeley, Imperial, and Dyson, here’s the pattern: • 𝗪𝗲𝗲𝗸 𝟭: “Our policy works perfectly in simulation!” • 𝗪𝗲𝗲𝗸 𝟰: “Why doesn’t this work on real objects?” • 𝗠𝗼𝗻𝘁𝗵 𝟮: “We basically need to retrain from scratch with real data.” 𝗧𝗵𝗲 𝗴𝗮𝗽 𝘀𝗶𝗺𝘂𝗹𝗮𝘁𝗶𝗼𝗻𝘀 𝗰𝗮𝗻’𝘁 𝗯𝗿𝗶𝗱𝗴𝗲: Unlike blind locomotion policies that can get away with sim-to-real transfer because they rely mainly on proprioception and contact forces, 𝘃𝗶𝘀𝗶𝗼𝗻-𝗴𝘂𝗶𝗱𝗲𝗱 𝗺𝗮𝗻𝗶𝗽𝘂𝗹𝗮𝘁𝗶𝗼𝗻 𝗶𝘀 𝗲𝘅𝘁𝗿𝗲𝗺𝗲𝗹𝘆 𝘀𝗲𝗻𝘀𝗶𝘁𝗶𝘃𝗲 𝘁𝗼 𝘃𝗶𝘀𝘂𝗮𝗹 𝗱𝗼𝗺𝗮𝗶𝗻 𝗴𝗮𝗽𝘀. • Real friction vs simulated surface textures • Manufacturing tolerances vs perfect CAD models • Dynamic lighting vs controlled virtual environments • Sensor noise vs instantaneous virtual readings 𝗛𝗲𝗿𝗲'𝘀 𝘄𝗵𝗮𝘁 𝗽𝗲𝗼𝗽𝗹𝗲 𝗱𝗼𝗻'𝘁 𝘁𝗮𝗹𝗸 𝗮𝗯𝗼𝘂𝘁: Building these detailed simulated environments takes forever. If it takes 7 days to build a simulated kitchen in simulation, wouldn't it be better to just collect real-world data in a real kitchen instead? 𝗗𝗼𝗻'𝘁 𝗴𝗲𝘁 𝗺𝗲 𝘄𝗿𝗼𝗻𝗴 - simulation is incredible for debugging, safety testing, and exploring edge cases. But it's not a magic solution to real-world deployment. 𝗪𝗵𝗮𝘁 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝘄𝗼𝗿𝗸𝘀: Use simulation strategically while making real-world data collection as efficient and flexible as possible. This is why Neuracore focuses on streamlined real-world data infrastructure. Because no amount of virtual training can replace understanding how your robot actually behaves in actual environments. 𝗧𝗵𝗲 𝗽𝗵𝘆𝘀𝗶𝗰𝘀 𝗼𝗳 𝘆𝗼𝘂𝗿 𝗱𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁 𝗲𝗻𝘃𝗶𝗿𝗼𝗻𝗺𝗲𝗻𝘁 𝗰𝗮𝗻'𝘁 𝗯𝗲 𝘀𝗶𝗺𝘂𝗹𝗮𝘁𝗲𝗱 𝗮𝘄𝗮𝘆. What’s been your experience with sim-to-real transfer?

Stephen James

25,347 次观看 • 10 个月前

(1/3) Sanctum has acquired Ironforge. At its peak, the Ironforge team handled up to one-third of all Solana transaction volume, making it one of the ecosystem’s most trusted transaction layers. The acquisition brings Ironforge’s battle-tested architecture and the team behind it into Sanctum, making our engineering team stronger than ever. “When it comes to building reliable infrastructure, the Ironforge team is unmatched, and their product completes our stack. Together, we’ve been able to build [redacted] in record time, which allows us to expand into a brand new vertical. They build like we do: fast, reliably, and with real conviction.” - fp 🌱 A full product reveal is imminent. Stay tuned for our next update!

Sanctum ☁️

78,024 次观看 • 1 年前