Скачать или смотреть Podcast : Expanding RL with Verifiable Rewards Across Diverse Domains by Tencent AI Lab

​In this episode, we delve into the paper "Expanding RL with Verifiable Rewards Across Diverse Domains" 🧠📄, authored by Yi Su, Dian Yu, Linfeng Song, Juntao Li, Haitao Mi, Zhaopeng Tu, Min Zhang, and Dong Yu. This research explores extending Reinforcement Learning with Verifiable Rewards (RLVR) beyond traditional areas like mathematics and coding, applying it to fields such as medicine, chemistry, psychology, and economics. The study demonstrates that RLVR can enhance reasoning capabilities across these diverse domains without the need for extensive domain-specific annotations. By fine-tuning a base 7B model using various RL algorithms against a distilled generative reward model, the researchers achieved policies that outperform state-of-the-art open-source aligned LLMs, highlighting RLVR's robustness and scalability.​

🔹 What You’ll Learn:

The fundamentals of Reinforcement Learning with Verifiable Rewards (RLVR)​

How RLVR is applied across diverse domains beyond traditional fields​
MarkTechPost

Insights into the methodologies and findings of the study​

The potential real-world applications and future implications of RLVR​

🌐 Why This Matters: As AI continues to evolve, enhancing reasoning abilities across various domains becomes crucial. Understanding RLVR offers insights into developing more intelligent, reliable, and efficient AI systems capable of tackling complex problems in diverse fields.​

Read the paper: https://arxiv.org/abs/2503.23829

📢 Don’t forget to LIKE, COMMENT, and SUBSCRIBE! 🔔 Hit the notification bell so you never miss an episode!

💬 Join the Discussion: What are your thoughts on applying RLVR across diverse domains? Drop a comment below!⬇️​

#RLVR #AI #MachineLearning #ReinforcementLearning #ArtificialIntelligence #Podcast