Mid-Training with Self-Generated Data Improves Reinforcement Learning in Language Models

📰 ArXiv cs.AI

Improve reinforcement learning in language models by incorporating self-generated data mid-training, enhancing diversity and effectiveness

advanced Published 12 May 2026
Action Steps
  1. Generate diverse self-generated data using the language model
  2. Incorporate the self-generated data into the reinforcement learning process mid-training
  3. Evaluate the performance of the model on reasoning tasks
  4. Compare the results with traditional reinforcement learning approaches
  5. Fine-tune the model as needed to optimize performance
Who Needs to Know This

Researchers and engineers working on large language models can benefit from this approach to improve the performance of their models, particularly in tasks that require diverse reasoning skills

Key Insight

💡 Incorporating self-generated data mid-training can improve the diversity and effectiveness of reinforcement learning in language models

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💡 Boost reinforcement learning in language models with self-generated data! #LLMs #RL

Key Takeaways

Improve reinforcement learning in language models by incorporating self-generated data mid-training, enhancing diversity and effectiveness

Full Article

Title: Mid-Training with Self-Generated Data Improves Reinforcement Learning in Language Models

Abstract:
arXiv:2605.08472v1 Announce Type: new Abstract: The effectiveness of Reinforcement Learning (RL) in Large Language Models (LLMs) depends on the nature and diversity of the data used before and during RL. In particular, reasoning problems can often be approached in multiple ways that rely on different forms of reasoning, and exposure to only a limited range of such approaches in the training data may limit the effectiveness of RL. Motivated by this, we investigate using diverse self-generated dat
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