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In flow matching, a coupling determines how noise and data samples are paired during training. The choice of coupling is important because it influences the geometry of trajectories at inference time. The simplest choice is the independent coupling, where noise and data points are paired arbitrarily. This can lead... show more

Alec Helbling

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65,253 görüntüleme • 2 ay önce •via X (Twitter)

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System identification (sysid) is the process of finding the physical parameters that make a simulation match reality. If you're training an RL locomotion policy in simulation, the accuracy of your motor model directly affects how well the policy transfers to the real robot. A recent git commit by Kevin Zakka added a sysid toolbox to MuJoCo which automates this process: you provide recorded motor data and a MuJoCo model, and it optimizes the model parameters to minimize the difference between simulated and real trajectories. For my RobStride Dynamics RS02 QDD motors (17 Nm peak, 7.75:1 gear), I built a Rust tool that sends multi-sine torque excitation at 1 kHz and records position/velocity feedback. I then feed this data into MuJoCo's sysid optimizer.

System identification (sysid) is the process of finding the physical parameters that make a simulation match reality. If you're training an RL locomotion policy in simulation, the accuracy of your motor model directly affects how well the policy transfers to the real robot. A recent git commit by Kevin Zakka added a sysid toolbox to MuJoCo which automates this process: you provide recorded motor data and a MuJoCo model, and it optimizes the model parameters to minimize the difference between simulated and real trajectories. For my RobStride Dynamics RS02 QDD motors (17 Nm peak, 7.75:1 gear), I built a Rust tool that sends multi-sine torque excitation at 1 kHz and records position/velocity feedback. I then feed this data into MuJoCo's sysid optimizer.

David Bar

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Holy sh!t ! OpenAI will have their custom inference chips ready in just a few months and deployed at scale by the end of the year! 🤯 Training chip = The heavy lifters that require massive amounts of data and power to build and teach the AI models from scratch. Inference chip = The specialized, highly efficient chips that actually run the AI and generate the answers in real-time when you use it. This is going to help OpenAI drastically cut down their massive compute costs, speed up model reasoning times, and finally break free from relying entirely on Nvidia to scale their operations.

Holy sh!t ! OpenAI will have their custom inference chips ready in just a few months and deployed at scale by the end of the year! 🤯 Training chip = The heavy lifters that require massive amounts of data and power to build and teach the AI models from scratch. Inference chip = The specialized, highly efficient chips that actually run the AI and generate the answers in real-time when you use it. This is going to help OpenAI drastically cut down their massive compute costs, speed up model reasoning times, and finally break free from relying entirely on Nvidia to scale their operations.

Chris

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This animation shows how a uniform grid of points is deformed by a flow matching model. The visualization highlights how the learned flow warps the underlying space—stretching, compressing, and bending it—to transform one probability distribution into another.

This animation shows how a uniform grid of points is deformed by a flow matching model. The visualization highlights how the learned flow warps the underlying space—stretching, compressing, and bending it—to transform one probability distribution into another.

Alec Helbling

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We’re proud to share that Tamarind Bio has been selected to build, host, and operate the inference infrastructure layer for TuneLab2.0, the next evolution of the platform. Eli Lilly and Company TuneLab is a first-of-its-kind, collaborative AI/ML drug discovery platform, bringing models trained on over $1B worth of Lilly proprietary data to the biotech ecosystem. Tamarind will power TuneLab’s scalable drug discovery workflows and model inference.

We’re proud to share that Tamarind Bio has been selected to build, host, and operate the inference infrastructure layer for TuneLab2.0, the next evolution of the platform. Eli Lilly and Company TuneLab is a first-of-its-kind, collaborative AI/ML drug discovery platform, bringing models trained on over $1B worth of Lilly proprietary data to the biotech ecosystem. Tamarind will power TuneLab’s scalable drug discovery workflows and model inference.

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The next World Cup star is playing right now, and nobody is watching. The 2026 World Cup is on, and millions of kids are dreaming of one day playing for their own flag. But the path there is almost impossible: no consolidated data, too many games, not enough scouts, human bias, and wrong place, wrong time. Any one of them can bury a generational talent before a scout ever sees them. That is what we are building to fix. So we developed a full automated annotation + action spotting model where it actually matters: grassroots football. Shaky cameras, awful angles, terrible lighting. But now nothing on the market beats sn44's v2.0 model on it. Check the last GT line, it’s human annotations. Simply because miners' outputs + post-process = magic. Post-process is a deliberate design choice. We point miners at the hardest 80% of the workflow, the part that is genuinely difficult to fake, and handle the rest downstream. Fewer places to game the system means a network you can trust. Every single challenge on sn44 can be an independent startup now. Let that sink in.

The next World Cup star is playing right now, and nobody is watching. The 2026 World Cup is on, and millions of kids are dreaming of one day playing for their own flag. But the path there is almost impossible: no consolidated data, too many games, not enough scouts, human bias, and wrong place, wrong time. Any one of them can bury a generational talent before a scout ever sees them. That is what we are building to fix. So we developed a full automated annotation + action spotting model where it actually matters: grassroots football. Shaky cameras, awful angles, terrible lighting. But now nothing on the market beats sn44's v2.0 model on it. Check the last GT line, it’s human annotations. Simply because miners' outputs + post-process = magic. Post-process is a deliberate design choice. We point miners at the hardest 80% of the workflow, the part that is genuinely difficult to fake, and handle the rest downstream. Fewer places to game the system means a network you can trust. Every single challenge on sn44 can be an independent startup now. Let that sink in.

Score - Subnet 44

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Amazon’s machine learning model collects 300 million data points per season and can now predict which players are likely to blitz before the snap. This is a look into the future of broadcasting.

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Joe Pompliano

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What if you kept asking an LLM to "make it better"? In some recent work at FAIR, we investigate how we can efficiently use RL to fine-tune LLMs to iteratively self-improve on their previous solutions at inference-time. Training for iterated self-improvement can be costly. The naive approach to training for K self-improvement steps leads to K times the number of rollout steps per episode. We introduce Exploratory Iteration (ExIt), an RL-based automatic curriculum method that bootstraps diverse training distributions of self-improvement tasks by upcycling the LLM's own responses at previous turns as the starting points for both self-improvement and *self-divergence.* In order to decide what task to train on next, the curriculum prioritizes sampling of partial turn histories that led to higher return variance in its GRPO group (a learnability score that comes for free). This automatic curriculum over the bootstrapped task space teaches the model how to perform iterated self-improvement while only ever training the model on single-step self-improvement tasks. We look at ExIt's impact in both single-turn (contest math problems) and multi-turn (BFCLv3 multi-turn tasks), as well as MLE-bench, where the LLM is run in a search scaffold to produce solutions to real Kaggle competitions. Across these eval settings, we find ExIt produces models with greater capacity for inference-time self-improvement compared to GRPO. Notably, ExIt models can self-improve on test tasks for many more steps than the typical solution depth encountered during training, including a 22% improvement in MLE-bench performance compared to GRPO.

What if you kept asking an LLM to "make it better"? In some recent work at FAIR, we investigate how we can efficiently use RL to fine-tune LLMs to iteratively self-improve on their previous solutions at inference-time. Training for iterated self-improvement can be costly. The naive approach to training for K self-improvement steps leads to K times the number of rollout steps per episode. We introduce Exploratory Iteration (ExIt), an RL-based automatic curriculum method that bootstraps diverse training distributions of self-improvement tasks by upcycling the LLM's own responses at previous turns as the starting points for both self-improvement and self-divergence. In order to decide what task to train on next, the curriculum prioritizes sampling of partial turn histories that led to higher return variance in its GRPO group (a learnability score that comes for free). This automatic curriculum over the bootstrapped task space teaches the model how to perform iterated self-improvement while only ever training the model on single-step self-improvement tasks. We look at ExIt's impact in both single-turn (contest math problems) and multi-turn (BFCLv3 multi-turn tasks), as well as MLE-bench, where the LLM is run in a search scaffold to produce solutions to real Kaggle competitions. Across these eval settings, we find ExIt produces models with greater capacity for inference-time self-improvement compared to GRPO. Notably, ExIt models can self-improve on test tasks for many more steps than the typical solution depth encountered during training, including a 22% improvement in MLE-bench performance compared to GRPO.

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OpenAI's Deep Research is getting a run for its money. Deep Lake was just released, and it's a different take on an AI system that can do deep research on your own data. You can use Deep Lake to build AI search with reasoning on your private and public data. (Look at the attached videos to get an idea of how it works.) If you want to research proprietary and sensitive data, Deep Research won't help you because it's limited to public data. Deep Lake, however, will allow you to use your private data. On top of that, Deep Lake supports multi-modal retrieval from the ground up. It uses vision language models for data ingestion and retrieval so that you can connect any data (PDFs, images, videos, structured data, etc.) You can even use mixed-data queries! Deep Lake can search your data from S3, Dropbox, and GCP. It learns from your queries over time, making the results as relevant to your work as possible!

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BIG NEWS. The Blockworks website has evolved: yesterday we were the home of news, today we are the home of onchain data. Head over to the site to see for yourself, but here's a little snippet of what you can expect: 1. Sector leaderboards (chains, DEXes, borrow lend, DATs, etc...) 2. Comprehensive data dashboards protocols 3. The ability to compare pricing and onchain data easily (coming soon) We're doing this because the industry still has a gigantic data problem. As investors get more sophisticated and fundamentals driven, basic high level facts are no longer sufficient. Investors need to be able to trust the data they are seeing and go much deeper than the surface level info that's available today. Additionally, because many data providers allow companies to essentially self report, you can't trust what you are seeing. This site is our contribution to fixing that problem and to ensuring clear, accurate data for investors. Blockworks is fully dedicated to becoming the most comprehensive data company in crypto in 2026. This is the first of many, many announcements like this this year, stay tuned.

Mippo 🟪

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This is the level of noise in Bibu! Eileen on the gate has a unique charm and screams like a wookie! Our vets are checking Klaas as he had fever this morning. Its very important to act on time because they can get infections quickly due to their open belly button! #アザラシ幼稚園

This is the level of noise in Bibu! Eileen on the gate has a unique charm and screams like a wookie! Our vets are checking Klaas as he had fever this morning. Its very important to act on time because they can get infections quickly due to their open belly button! #アザラシ幼稚園

Panka

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Fancy winning a pair of #EFL tickets for a home game of your choice? 😍 Reply with the answer to the following question and use #Super6Christmas for a chance to win 🎟️ How many points are Leicester City on in the league this season? 🦊 Winner announced at 4pm, T&Cs apply ⌚️

Fancy winning a pair of #EFL tickets for a home game of your choice? 😍 Reply with the answer to the following question and use #Super6Christmas for a chance to win 🎟️ How many points are Leicester City on in the league this season? 🦊 Winner announced at 4pm, T&Cs apply ⌚️

Super 6

53,493 görüntüleme • 2 yıl önce

At Dreamforce, the exploration of the possibilities of AI is incredible. At the same time, it's important that we understand that policies never move at the same speed as technology. So having a grasp over what is happening and how the negative aspects of AI don't proliferate in a big way, to minimize collateral damage is most important. I hope the companies and governments across the world wake up to this and ensure the best of AI possibilities will reach the people, as this is a phenomenal enabler for humanity like never before. -Sg Marc Benioff Dreamforce Salesforce #DF24

At Dreamforce, the exploration of the possibilities of AI is incredible. At the same time, it's important that we understand that policies never move at the same speed as technology. So having a grasp over what is happening and how the negative aspects of AI don't proliferate in a big way, to minimize collateral damage is most important. I hope the companies and governments across the world wake up to this and ensure the best of AI possibilities will reach the people, as this is a phenomenal enabler for humanity like never before. -Sg Marc Benioff Dreamforce Salesforce #DF24

Sadhguru

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Why is the medical community staying silent? I asked if other providers are reporting vaccine injuries to VAERS, and the answer was a staggering "no." If we aren’t tracking the data, how can we ensure patient safety? With over 19,500 deaths reported in the system, it's time to start asking why the burden of reporting is falling on so few.

Why is the medical community staying silent? I asked if other providers are reporting vaccine injuries to VAERS, and the answer was a staggering "no." If we aren’t tracking the data, how can we ensure patient safety? With over 19,500 deaths reported in the system, it's time to start asking why the burden of reporting is falling on so few.

Mary Talley Bowden MD

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Depth Any Video with Scalable Synthetic Data AI physicists and chemists continue to make strides in depth estimation from video. Check out this new paper featuring some impressive examples. See the thread for more details (unfortunately no code yet). Abstract: Video depth estimation has long been hindered by the scarcity of consistent and scalable ground truth data, leading to inconsistent and unreliable results. In this paper, we introduce Depth Any Video, a model that tackles the challenge through two key innovations. First, we develop a scalable synthetic data pipeline, capturing real-time video depth data from diverse game environments, yielding 40,000 video clips of 5-second duration, each with precise depth annotations. Second, we leverage the powerful priors of generative video diffusion models to handle real-world videos effectively, integrating advanced techniques such as rotary position encoding and flow matching to further enhance flexibility and efficiency. Unlike previous models, which are limited to fixed-length video sequences, our approach introduces a novel mixed-duration training strategy that handles videos of varying lengths and performs robustly across different frame rates 0 - even on single frames. At inference, we propose a depth interpolation method that enables our model to infer high-resolution video depth across sequences of up to 150 frames. Our model outperforms all previous generative depth models in terms of spatial accuracy and temporal consistency.

Depth Any Video with Scalable Synthetic Data AI physicists and chemists continue to make strides in depth estimation from video. Check out this new paper featuring some impressive examples. See the thread for more details (unfortunately no code yet). Abstract: Video depth estimation has long been hindered by the scarcity of consistent and scalable ground truth data, leading to inconsistent and unreliable results. In this paper, we introduce Depth Any Video, a model that tackles the challenge through two key innovations. First, we develop a scalable synthetic data pipeline, capturing real-time video depth data from diverse game environments, yielding 40,000 video clips of 5-second duration, each with precise depth annotations. Second, we leverage the powerful priors of generative video diffusion models to handle real-world videos effectively, integrating advanced techniques such as rotary position encoding and flow matching to further enhance flexibility and efficiency. Unlike previous models, which are limited to fixed-length video sequences, our approach introduces a novel mixed-duration training strategy that handles videos of varying lengths and performs robustly across different frame rates 0 - even on single frames. At inference, we propose a depth interpolation method that enables our model to infer high-resolution video depth across sequences of up to 150 frames. Our model outperforms all previous generative depth models in terms of spatial accuracy and temporal consistency.

MrNeRF

27,428 görüntüleme • 1 yıl önce

🚨 BREAKING: Gravity doesn’t actually “pull” you. That’s just how it looks. What’s really happening is deeper… Space-time isn’t empty. It behaves more like a field with structure. And when mass appears… it doesn’t just “bend space” it changes how time flows. Think of it like this: If time flows evenly → everything stays stable If time compresses → motion is forced toward that region That motion is what we call gravity. In Einstein’s model: mass curves space-time But here’s the missing piece: What is doing the curving? In my framework: Mass = a region where time is compressed Gravity = objects moving along time gradients Motion = the system trying to rebalance So you’re not being “pulled down”… You’re being carried along a flow of time. This explains something most people miss: Gravity always points one way. Because time only flows one way. And the deeper implication is wild: If gravity is just time imbalance… then controlling time flow = controlling gravity. The real question is: Are we discovering gravity… or learning how to engineer it? Follow for more this goes deeper.

🚨 BREAKING: Gravity doesn’t actually “pull” you. That’s just how it looks. What’s really happening is deeper… Space-time isn’t empty. It behaves more like a field with structure. And when mass appears… it doesn’t just “bend space” it changes how time flows. Think of it like this: If time flows evenly → everything stays stable If time compresses → motion is forced toward that region That motion is what we call gravity. In Einstein’s model: mass curves space-time But here’s the missing piece: What is doing the curving? In my framework: Mass = a region where time is compressed Gravity = objects moving along time gradients Motion = the system trying to rebalance So you’re not being “pulled down”… You’re being carried along a flow of time. This explains something most people miss: Gravity always points one way. Because time only flows one way. And the deeper implication is wild: If gravity is just time imbalance… then controlling time flow = controlling gravity. The real question is: Are we discovering gravity… or learning how to engineer it? Follow for more this goes deeper.

TheNewPhysics

21,772 görüntüleme • 3 ay önce

Contrail lesson! 1. “Chemtrails” don’t exist. Just to get that out of the way. 2. Observe the satellite loop and Skew-T chart. In the IR satellite loop you can see yesterday, the West Coast had a decent short wave ridge suppressing moisture over California and Nevada. Today, you can see moisture from a low pressure over the Pacific spilling over the ridge that is now moving east of California. This is upper level moisture ADVECTING into the area. This upper level moisture is mainly above the 500mb level, or 20,000ft. 3. Now observe the Skew-T chart. Particularly clue into the 300mb level. This is a perfect example of what I talk about all the time, and why it’s important to pay attention to the 300mb level. This moisture layer is advecting particularly at the 300mb level, and synoptic scale cirrus development, and advection, typically occurs at 300mb. This is key because aircraft are flying at and above the 300mb level. 4. So, lastly, observe the pictures that I took of the sky over northern Nevada at the time of this post. You can see the layer of cirrus as well as contrails persisting in that moisture layer, exactly as depicted in the satellite shot AND confirmed by the Skew-T chart. Keep in mind that temperatures at this level of the atmosphere are typically -20 to -50°C. In this case, you can see that the temperature at 300mb is -40°C and relative humidities at this level are far different than what you experience at the surface. Any decrease in the gap between temperature and dewpoint at this level can significantly increase the relative humidity. This is why it’s referred to as “relative”because it’s far different than temperatures and dew points at the surface. So, to bring it all together, aircraft flying at these altitudes, which most commercial and military aircraft do, injecting warm, moist air from the engines rapidly into the super cooled environment, not only instantly form contrails, but when relative humidities are as depicted in this example, will enable contrails to persist for hours at a time supported by the moisture existing in that layer. This is what causes persistent contrails. These ARE NOT “chemtrails” and because they persist, does not, and will not ever, make them “chemtrails.” Now that you all needed your government to tell you that climate change was a hoax and I’ve been telling you for years that the “Geoengineering” and “chemtrail” nonsense are propaganda directly related to the climate change hoax, hopefully you can take some time to learn the basics of the atmosphere and understand what I’m showing you here, and how it works, so you’re not fooled by climate propaganda going forward. Thank you for your attention to this matter. 💪🏼🇺🇸

Contrail lesson! 1. “Chemtrails” don’t exist. Just to get that out of the way. 2. Observe the satellite loop and Skew-T chart. In the IR satellite loop you can see yesterday, the West Coast had a decent short wave ridge suppressing moisture over California and Nevada. Today, you can see moisture from a low pressure over the Pacific spilling over the ridge that is now moving east of California. This is upper level moisture ADVECTING into the area. This upper level moisture is mainly above the 500mb level, or 20,000ft. 3. Now observe the Skew-T chart. Particularly clue into the 300mb level. This is a perfect example of what I talk about all the time, and why it’s important to pay attention to the 300mb level. This moisture layer is advecting particularly at the 300mb level, and synoptic scale cirrus development, and advection, typically occurs at 300mb. This is key because aircraft are flying at and above the 300mb level. 4. So, lastly, observe the pictures that I took of the sky over northern Nevada at the time of this post. You can see the layer of cirrus as well as contrails persisting in that moisture layer, exactly as depicted in the satellite shot AND confirmed by the Skew-T chart. Keep in mind that temperatures at this level of the atmosphere are typically -20 to -50°C. In this case, you can see that the temperature at 300mb is -40°C and relative humidities at this level are far different than what you experience at the surface. Any decrease in the gap between temperature and dewpoint at this level can significantly increase the relative humidity. This is why it’s referred to as “relative”because it’s far different than temperatures and dew points at the surface. So, to bring it all together, aircraft flying at these altitudes, which most commercial and military aircraft do, injecting warm, moist air from the engines rapidly into the super cooled environment, not only instantly form contrails, but when relative humidities are as depicted in this example, will enable contrails to persist for hours at a time supported by the moisture existing in that layer. This is what causes persistent contrails. These ARE NOT “chemtrails” and because they persist, does not, and will not ever, make them “chemtrails.” Now that you all needed your government to tell you that climate change was a hoax and I’ve been telling you for years that the “Geoengineering” and “chemtrail” nonsense are propaganda directly related to the climate change hoax, hopefully you can take some time to learn the basics of the atmosphere and understand what I’m showing you here, and how it works, so you’re not fooled by climate propaganda going forward. Thank you for your attention to this matter. 💪🏼🇺🇸

Dylan Tucker

26,804 görüntüleme • 8 ay önce

What if a network could deliver AI results closer to where data is created, instead of sending it to far‑off data centers? In collaboration with NVIDIA and Decart, we’re making that a reality by bringing GPU‑powered computing to the network edge — closer to homes and businesses — so AI applications respond in real-time. This is how we're laying the foundation for the next generation of AI‑driven services:

What if a network could deliver AI results closer to where data is created, instead of sending it to far‑off data centers? In collaboration with NVIDIA and Decart, we’re making that a reality by bringing GPU‑powered computing to the network edge — closer to homes and businesses — so AI applications respond in real-time. This is how we're laying the foundation for the next generation of AI‑driven services:

Comcast

15,350 görüntüleme • 3 ay önce

I'm generally curious how you can have two 'truths' at the same time. Michelle Wu claims that: 1. Boston is the safest major city in the country, safe for everyone, and illegal immigrants do not contribute to the crime. Areas are not 'less safe' because of this. 2. Boston law enforcement does not collect immigration status during arrests. So my question is, if law enforcement does not track or collect immigration status, as she claims, how can the city confidently claim that illegal immigrants are not contributing to crime without the data?

I'm generally curious how you can have two 'truths' at the same time. Michelle Wu claims that: 1. Boston is the safest major city in the country, safe for everyone, and illegal immigrants do not contribute to the crime. Areas are not 'less safe' because of this. 2. Boston law enforcement does not collect immigration status during arrests. So my question is, if law enforcement does not track or collect immigration status, as she claims, how can the city confidently claim that illegal immigrants are not contributing to crime without the data?

Mike Urban

26,225 görüntüleme • 1 yıl önce

Awakening means the realisation that much of the reality presented to you is a Lie. Critical thinking is the repetition of asking ‘how’ & ‘why’ until any given concept makes sense. With these two points in mind let me pose a question to you all…..(many will be triggered by the question alone) We are told the Universe is expanding at 160,000mph & that the Great Pyramids are 4,500 years old (other estimates suggest up to 10,000 years). The pyramids were constructed to align with 3 stars that sit across the constellation Orion’s Belt. Many other famous archeological landmarks are also aligned with stars. ALL these landmarks still to this very day perfectly align with the same stars, in exactly the same way since their inception. WHY if the universe is expanding at this extraordinary rate, & therefore both Earth & stars are accelerating at different trajectories - do they still align perfectly today, just as they did thousands of years ago? Regardless of how big the Universe may or may not be - surely this velocity & period of time would mean a change of position, even if ever so slight - would it not? It’s a fair question I think & look forward to your answers……

Awakening means the realisation that much of the reality presented to you is a Lie. Critical thinking is the repetition of asking ‘how’ & ‘why’ until any given concept makes sense. With these two points in mind let me pose a question to you all…..(many will be triggered by the question alone) We are told the Universe is expanding at 160,000mph & that the Great Pyramids are 4,500 years old (other estimates suggest up to 10,000 years). The pyramids were constructed to align with 3 stars that sit across the constellation Orion’s Belt. Many other famous archeological landmarks are also aligned with stars. ALL these landmarks still to this very day perfectly align with the same stars, in exactly the same way since their inception. WHY if the universe is expanding at this extraordinary rate, & therefore both Earth & stars are accelerating at different trajectories - do they still align perfectly today, just as they did thousands of years ago? Regardless of how big the Universe may or may not be - surely this velocity & period of time would mean a change of position, even if ever so slight - would it not? It’s a fair question I think & look forward to your answers……

Concerned Citizen

18,266 görüntüleme • 2 yıl önce

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

52,300 görüntüleme • 2 yıl önce