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1/ Introducing RL Swarm’s new backend: GenRL. A modular reinforcement learning library built for distributed, fault-tolerant training - now powering RL Swarm from the ground up. 🧵

gensyn

93,756 subscribers

82,191 次观看 • 1 年前 •via X (Twitter)

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gensyn 的头像

gensyn1 年前

2/ Each worker runs its own environment instance, contributes asynchronously to a shared rollout buffer, and updates its model weights independently, so no central controller is required.

gensyn 的头像

gensyn1 年前

3/ GenRL allows RL Swarm to work with any environment, described intuitively through code. This launch incorporates Reasoning Gym out-of-the-box, giving access to >100 community-created environments with no extra configuration required.

gensyn 的头像

gensyn1 年前

4/ What’s new: – Modular GenRL backend – Expanded configuration surface – Prebuilt Docker image for easy deployment – Reasoning Gym environment to enhance model reasoning capabilities – New multi-task swarm

gensyn 的头像

gensyn1 年前

5/ Now live on the Gensyn testnet. You can run RL-Swarm with GenRL today. Full code + setup:

gensyn 的头像

gensyn1 年前

6/ A node update is required for GenRL. Please visit ⁠support-discussion in the Discord if you have any questions.

Gautamgg 🕵 的头像

Gautamgg 🕵1 年前

I want to ask 1 que What about previous trained model data rewards & participants bec it's not showing Is that data saved in your database? @fenbielding @_jamico @_grieve waiting for ans 💙

Mintair | One Click Node🪄 的头像

Mintair | One Click Node🪄1 年前

Looks really interesting, we gotta setup our own custom environment.

AJDominic (🐱,🐐) 的头像

AJDominic (🐱,🐐)1 年前

What gensyn cooking is unmatched!

Bitduke 的头像

Bitduke1 年前

Cool, cool - more modularity

lior.eth (Lior Messika) 的头像

lior.eth (Lior Messika)1 年前

These retro vibes are everything I ever wanted from an AI lab

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The network for machine intelligence Two years ago, we laid out our vision for a machine learning compute protocol. One that connects every device in the world into an open network for machine intelligence, with no gatekeepers or artificial boundaries. This week, we’ll be sharing some of our early progress, beginning with RL Swarm, a peer-to-peer system for collaborative reinforcement learning over the internet. Next month, we’ll open our Testnet, allowing anyone to contribute to the frontier of open machine intelligence. Introducing RL Swarm RL Swarm is a fully open source system for collaborative reinforcement learning over the internet. It is a live demo of our research findings, which show that models training with RL learn faster when they train as a collective swarm than they do on their own. Join our swarm now to see this in practice. You can participate with consumer hardware at home or a powerful GPU in the cloud. You can follow along with the swarm’s progress by following the links below.

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228,906 次观看 • 1 年前

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58,901 次观看 • 1 个月前

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So we did a bunch of projects with real world reinforcement learning - but it was often too inefficient to be practical to train tabula rasa. This suggests we need better priors, but acquiring these from on-robot data can often be expensive as well. In our recent work, we show that despite being fundamentally inaccurate, simulation can guide provide a cheap way to guide real-world RL finetuning to be super efficient! We propose Simulation-Guided Fine-Tuning (SGFT) - a simple paradigm for sim2real finetuning that uses simulation to provide reward shaping that accelerates real world RL finetuning beyond just providing an initialization. TLDR: Use value functions from sim to shape rewards for real-world RL, see large sample efficiency improvements 🧵(1/6)

Abhishek Gupta

13,637 次观看 • 1 年前