Mid-Training with Self-Generated Data Improves Reinforcement Learning in Language Models
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 data during mid-training as an intermediate step before RL training. Specifically, we adopt a bootstrapped data-generation framework guided by George Polya's problem-solving approaches for generating multiple variants of correct answers for each qu...
| Authors | Aswin RRV, Jacob Dineen, Divij Handa et al. |
|---|---|
| Primary category | cs.AI |
| Published | 8 May 2026 |
| Last updated at source | 8 May 2026 |
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