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To Mix or To Merge: Toward Multi-Domain Reinforcement Learning for Large Language Models
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To Mix or To Merge: Toward Multi-Domain Reinforcement Learning for Large Language Models

#Reinforcement Learning #Large Language Models #Multi-Domain #RLVR #Artificial Intelligence #Machine Learning #Expert Systems #arXiv

📌 Key Takeaways

  • Researchers propose new methods for multi-domain reinforcement learning in large language models
  • Current models primarily use two approaches for handling multiple domains
  • RLVR has shown effectiveness in specific domains like coding and mathematics
  • The research aims to create general expert-level AI systems across diverse fields

📖 Full Retelling

Researchers announced a new approach to multi-domain reinforcement learning for large language models on arXiv on February 26, 2026, addressing the challenge of creating general expert-level AI systems across different domains. The paper, titled 'To Mix or To Merge: Toward Multi-Domain Reinforcement Learning for Large Language Models,' examines how Reinforcement Learning with Verifiable Rewards (RLVR) can be effectively applied across multiple domains rather than just specialized areas like coding or mathematics. Current state-of-the-art models primarily utilize two different approaches when dealing with multiple domains, but the researchers suggest that more sophisticated methods are needed to achieve true multi-domain expertise. This research represents a significant step forward in developing AI systems that can perform at expert levels across diverse fields rather than being limited to narrow domains.

🏷️ Themes

Artificial Intelligence, Machine Learning, Multi-Domain Systems

📚 Related People & Topics

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Entity Intersection Graph

Connections for Reinforcement learning:

🌐 Large language model 7 shared
🌐 Artificial intelligence 5 shared
🌐 Machine learning 3 shared
🏢 Science Publishing Group 2 shared
🌐 Reasoning model 2 shared
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Original Source
arXiv:2602.12566v1 Announce Type: new Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) plays a key role in stimulating the explicit reasoning capability of Large Language Models (LLMs). We can achieve expert-level performance in some specific domains via RLVR, such as coding or math. When a general multi-domain expert-level model is required, we need to carefully consider the collaboration of RLVR across different domains. The current state-of-the-art models mainly employ two dif
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Source

arxiv.org

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