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The document presents SPELL, a self-play reinforcement learning framework designed to enhance long-context reasoning in large language models (LLMs) without relying on human annotations. By integrating the roles of questioner, responder, and verifier, SPELL enables continuous self-improvement and utilizes an automated curriculum to adapt task difficulty. Experimental results demonstrate that SPELL significantly outperforms traditional methods and achieves notable performance gains across various LLMs on lon

Author

隋辨

Language

EN