Top Tweets for #ProtocolLearning
Just last month at our #ProtocolLearning workshop at @iclr_conf in Rio I made the point that the current concentration of power of AI across USA and China will be used for coercion and weaponized.
@Pluralis exists to open third path of collective AI development.
The path we're on should now be evident to everyone.
Access to AI will be used as instrument of coercion by the USA and Chinese governments.
We do need a third path, #ProtocolLearning, #DecentralizedAI for a Collective, Trustless, Sovereign AI.
The US government, citing national security authorities, has issued an export control directive to suspend all access to Fable 5 and Mythos 5 by any foreign national, whether inside or outside the United States, including foreign national Anthropic employees.
The net effect of this order is that we must abruptly disable Fable 5 and Mythos 5 for all our customers to ensure compliance.
Access to all other Claude models is not affected.
We apologize for this disruption to our customers. We believe this is a misunderstanding and are working to restore access as soon as possible.
Read our full statement: https://t.co/bwn0sximKZ
Everyone talks about “open AI,” but few are actually building it.
Pluralis is changing that rule — by training models in a distributed manner, without a single owner.
Where true AI decentralization begins
@PluralisHQ
#ProtocolLearning
#AIRevolution
Hey! Just a heads-up—WiEP Cohort 4 is almost full.
Ready to learn, build, and grow in a vibrant community? Join us as a participant in our upcoming cohort 4!
Apply now : https://t.co/xJsBAGEKan #Web3Education #Cohort4 #LearnWeb3 #Web3Mentorship #ProtocolLearning
🌐 AI as a Protocol — Not a Product @PluralisHQ 🤖
Why rent access to intelligence when you can help build it?
Pluralis turns AI into open infrastructure:
🧠 No owners
💻 No API locks
🌍 Just a protocol anyone can join
The future of AI is public.
#ProtocolLearning #AI #Web3 #DecentralizedAI
Pluralis Research is solving open-source AI’s funding crisis with Protocol Learning: contributors earn as they build, with no upfront costs and a shared value model. Open, sustainable, and finally fair for all.
#AI #Web3 #OpenSourceAI #ProtocolLearning @PluralisHQ
Probably biggest week in Decentralized Training to date off back of ICLR and more about to come out. Summary of situation as it stands today:
1. Decentralized RL post-training is clearly working. @gensynai the latest with great results here. This process takes a strong base model, gives copies to participants who generate reasoning traces which are then collected and used to improve the base model. This obviously relies on the base models being available/open-weight, and is significantly cheaper than Pretraining. Nodes only need to do inference. The drawback is there is mounting evidence (and it is very intuitive) that it is not possible to RL your way past a bad base model. So you retain a dependency. We need to wait for the results of these runs, but the reality is this is going to work one way or another because the process is so trivially parallizable.
2. Data-Parallel (DP) pretraining looks good. Both @NousResearch and @PrimeIntellect already have results here at 10B scale model sizes. It will be very straightforward (but expensive for node operators) to extend this to the 100B case. This is because in DP every node keeps a full copy of the model, so you need for example 8xh100s to train at the 10b size; you can’t use small cards. So you can just extend this technique by scaling up the nodes and do cross-datacenter collaborative training (i.e. every node is comprised of 100 H100’s or so, and you train >100b model). You also have the problem that everyone sees a full copy of model so not clear how to monetize (Protocol Learning solves this).
3. Model-Parallel (where the model itself is split over nodes - think 1000 geographically seperate Macbooks training a 100b param model, where each device only has a small portion of total model) started to show the first inklings of being possible. We (@PluralisHQ) published the ‘Beyond Top k' paper which compresses comms between nodes over 90%, as well as two other works that show you can use heterogenous devices in a Pipeline Parallel (PP) setup. We also had our Nesterov method for PP accepted into ICML2025, which as far as I'm aware of is the first paper on decentralized training accepted into a major AI conference since the original SWARM paper, and should help catalyse interest from mainstream AI circles.
Is decentralized model-parallel solved → NO. The communication bandwidth is so much worse compared to a datacenter, that even 90% is not enough. We need to get to around 300x compression to hit parity with centralised training. There remains a huge question if this is even possible - you are destroying so much of the training signal by doing this. This is Pluralis’s focus.
However what happens if this works? For the first time, you can do real collaborative pretraining. There is no dependence on deepseek or Meta. Individuals can combine compute to create models at this scale, from scratch. We get actual community-driven innovation happening here in a way that has never existed to date. Decentralised RL-based post-training can then be used to make these models even better.
The reality is we are at the earliest days of something hugely significant occurring here. This is going to be a major field. The above companies are firing on all cylinders, a bunch more is about to come out of the gate shortly, and I don’t expect this to slow down at all from now until whatever happens happens. And if your reading this you’re early.
Most decentralized AI systems break down the moment a device lags or drops out.
Pluralis’ secret?
A protocol called SWARM — now upgraded with asynchronous parallelism that keeps training moving even when the network’s a mess.
#Pluralis #ProtocolLearning #AIInfra @alexanderlong
Meow! 🐾 Check out my fun video about the @PluralisHQ Reading Group! Dive into AI & decentralized learning with cool folks weekly on Discord. Join the knowledge hunt!
@alexanderlong
@uriel_web3
#AI #ProtocolLearning #DecentralizedAI #ReadingGroup
💭 Curious how decentralized AI can actually work (without pain or data centers)?
This thread breaks down how Pluralis does it: async logic, resilience, compression & scale.
If you learn something — like or repost!
👇
#Pluralis #AIInfrastructure #DecentralizedAI #ProtocolLearning
Open-source AI's funding crisis? 🤔 @PluralisHQ's Protocol Learning just might be the answer. Contributors earn, NO upfront costs & shared value. Finally, open AND sustainable. This is how we win.
#AI #Web3 #OpenSourceAI #ProtocolLearning
🤖 Pluralis (@PluralisHQ) is reimagining AI training with Protocol Learning—open, fair, and collective. No closed doors, no broken open-source, just real collaboration to build better models together.
#AI #ProtocolLearning #Pluralis https://t.co/J3ZFXrMOS8
We propose a new Momentum Look-Ahead algorithm at ICLR'25 MCDC which allows heterogenous GPUs to be used with high utilization in decentralized pretraining. Baselines are Async-DiloCo and DyLU which we outperform. https://t.co/mbbL9CE3fb
Explore #protocollearning for #6G! 🌐 #CENTRIC’s repository uses #MuJoCo #robots to design smarter #communication systems. Check it out:
🔗 https://t.co/7sHRkBvwcC
#AI #Robotics #Innovation
Glad to be joining @Pluralis__ as a Founding Scientist to challenge the centralized training paradigm and open up the world to large-scale decentralized training! Big things are coming - stay tuned! #ProtocolLearning #PluralisAI https://t.co/W2U9uqjptr
Bit of personal news 🚀 As of last week, I left Amazon and joined the amazing team at @Pluralis__ as a Founding Scientist. Excited about going back to optimisation and exploring the future of large-scale decentralized training. #ProtocolLearning #PluralisAI
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