🤝 We invite you to join us for this week's RL Journal Club session, where we will explore the intriguing synergies between Reinforcement Learning (RL) and Large Language Models (LLMs). This session will delve into how these two powerful fields intersect, offering new perspectives and opportunities for advancement in AI research.
✅This Week's Presentation:
🔹 Title: Synergies Between RL and LLMs 🔸 Presenter: Moein Salimi 🌀 Abstract: In this presentation, we will review research studies that combine Reinforcement Learning (RL) and Large Language Models (LLMs), two domains that have been significantly propelled by deep neural networks. The discussion will center around a novel taxonomy proposed in the paper, categorizing the interaction between RL and LLMs into three main classes: RL4LLM, where RL enhances LLM performance in NLP tasks; LLM4RL, where LLMs assist in training RL models for non-NLP tasks; and RL+LLM, where both models work together within a shared planning framework. The presentation will explore the motivations behind these synergies, their successes, potential challenges, and avenues for future research.
The presentation will be based on the following paper:
▪️The RL/LLM Taxonomy Tree: Reviewing Synergies Between Reinforcement Learning and Large Language Models (https://arxiv.org/abs/2402.01874)
☝️ Note: The discussion is open to everyone, but we can only host students of Sharif University of Technology in person.
💯 This session promises to be an enlightening exploration of how RL and LLMs can work together to push the boundaries of AI research. Don’t miss this opportunity to deepen your understanding and engage in thought-provoking discussions!
🤝 We invite you to join us for this week's RL Journal Club session, where we will explore the intriguing synergies between Reinforcement Learning (RL) and Large Language Models (LLMs). This session will delve into how these two powerful fields intersect, offering new perspectives and opportunities for advancement in AI research.
✅This Week's Presentation:
🔹 Title: Synergies Between RL and LLMs 🔸 Presenter: Moein Salimi 🌀 Abstract: In this presentation, we will review research studies that combine Reinforcement Learning (RL) and Large Language Models (LLMs), two domains that have been significantly propelled by deep neural networks. The discussion will center around a novel taxonomy proposed in the paper, categorizing the interaction between RL and LLMs into three main classes: RL4LLM, where RL enhances LLM performance in NLP tasks; LLM4RL, where LLMs assist in training RL models for non-NLP tasks; and RL+LLM, where both models work together within a shared planning framework. The presentation will explore the motivations behind these synergies, their successes, potential challenges, and avenues for future research.
The presentation will be based on the following paper:
▪️The RL/LLM Taxonomy Tree: Reviewing Synergies Between Reinforcement Learning and Large Language Models (https://arxiv.org/abs/2402.01874)
☝️ Note: The discussion is open to everyone, but we can only host students of Sharif University of Technology in person.
💯 This session promises to be an enlightening exploration of how RL and LLMs can work together to push the boundaries of AI research. Don’t miss this opportunity to deepen your understanding and engage in thought-provoking discussions!
Telegram auto-delete message, expiring invites, and more
elegram is updating its messaging app with options for auto-deleting messages, expiring invite links, and new unlimited groups, the company shared in a blog post. Much like Signal, Telegram received a burst of new users in the confusion over WhatsApp’s privacy policy and now the company is adopting features that were already part of its competitors’ apps, features which offer more security and privacy. Auto-deleting messages were already possible in Telegram’s encrypted Secret Chats, but this new update for iOS and Android adds the option to make messages disappear in any kind of chat. Auto-delete can be enabled inside of chats, and set to delete either 24 hours or seven days after messages are sent. Auto-delete won’t remove every message though; if a message was sent before the feature was turned on, it’ll stick around. Telegram’s competitors have had similar features: WhatsApp introduced a feature in 2020 and Signal has had disappearing messages since at least 2016.
Export WhatsApp stickers to Telegram on Android
From the Files app, scroll down to Internal storage, and tap on WhatsApp. Once you’re there, go to Media and then WhatsApp Stickers. Don’t be surprised if you find a large number of files in that folder—it holds your personal collection of stickers and every one you’ve ever received. Even the bad ones.Tap the three dots in the top right corner of your screen to Select all. If you want to trim the fat and grab only the best of the best, this is the perfect time to do so: choose the ones you want to export by long-pressing one file to activate selection mode, and then tapping on the rest. Once you’re done, hit the Share button (that “less than”-like symbol at the top of your screen). If you have a big collection—more than 500 stickers, for example—it’s possible that nothing will happen when you tap the Share button. Be patient—your phone’s just struggling with a heavy load.On the menu that pops from the bottom of the screen, choose Telegram, and then select the chat named Saved messages. This is a chat only you can see, and it will serve as your sticker bank. Unlike WhatsApp, Telegram doesn’t store your favorite stickers in a quick-access reservoir right beside the typing field, but you’ll be able to snatch them out of your Saved messages chat and forward them to any of your Telegram contacts. This also means you won’t have a quick way to save incoming stickers like you did on WhatsApp, so you’ll have to forward them from one chat to the other.
