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About this paper
Caijun Xu, Changyi Xiao, Zhongyuan Peng, Xinrun Wang, Yixin Cao
A framework called SCALER, developed by researchers at Fudan University and Singapore Management University, sustains effective learning for large language models by adaptively synthesizing and curating difficulty-controllable reasoning environments. It enabled a Qwen3-4B-Base model to achieve an average accuracy of 54.25% across five reasoning benchmarks, outperforming static dataset and existing environment-based methods.
Key insights
- Sustained learning and improved generalization for LLMs are achieved by co-adapting the training environments to the model's evolving capabilities, preventing reward sparsity and overfitting.
- Both the adaptive difficulty control within environments and the dynamic curation of environment diversity are essential components for achieving performance gains and prolonged learning.
- The framework can scale to a large number of environments, with increasing environment diversity leading to incremental performance improvements for LLM reasoning capabilities.
Summary by alphaXiv.
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