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🧠 RL Journal Club: This Week's Session

🤝 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)

Session Details:

📅 Date: Tuesday
🕒 Time: 3:30 - 5:00 PM
🌐 Location: Online at https://vc.sharif.edu/ch/rohban
📍 For in-person attendance, please message me on Telegram at @alirezanobakht78

☝️ 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 look forward to your participation!

#RLJClub #JClub #RIML #SUT #AI #RL #LLM



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🧠 RL Journal Club: This Week's Session

🤝 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)

Session Details:

📅 Date: Tuesday
🕒 Time: 3:30 - 5:00 PM
🌐 Location: Online at https://vc.sharif.edu/ch/rohban
📍 For in-person attendance, please message me on Telegram at @alirezanobakht78

☝️ 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 look forward to your participation!

#RLJClub #JClub #RIML #SUT #AI #RL #LLM

BY RIML Lab




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telegram Telegram | DID YOU KNOW?

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What is Telegram?

Telegram’s stand out feature is its encryption scheme that keeps messages and media secure in transit. The scheme is known as MTProto and is based on 256-bit AES encryption, RSA encryption, and Diffie-Hellman key exchange. The result of this complicated and technical-sounding jargon? A messaging service that claims to keep your data safe.Why do we say claims? When dealing with security, you always want to leave room for scrutiny, and a few cryptography experts have criticized the system. Overall, any level of encryption is better than none, but a level of discretion should always be observed with any online connected system, even Telegram.

Importantly, that investor viewpoint is not new. It cycles in when conditions are right (and vice versa). It also brings the ineffective warnings of an overpriced market with it.Looking toward a good 2022 stock market, there is no apparent reason to expect these issues to change.

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