arXiv:2605.31433v1 Announce Type: new Abstract: Self-play can train language models without external supervision. However, existing methods require rule-checkable answers, leaving open-ended tasks dependent on curated prompts or frontier-model judges. We introduce SCOPE, a data-free self-play framework for open-ended tasks that co-evolves two policies: a Challenger that generates document-grounded tasks, and a Solver that answers them through multi-turn retrieval. A frozen copy of the initial model serves as the self-judge, which writes task-specific rubrics from the source document and grades
The proliferation of advanced language models necessitates more robust and data-efficient training methods, particularly for open-ended tasks where curated datasets are scarce or difficult to construct.
This development addresses a critical limitation in training sophisticated AI models, broadening the applicability of self-play to complex, open-ended problems without reliance on human supervision or frontier models for judgment.
AI models will be able to learn and improve on a wider array of creative and nuanced tasks, potentially reducing the need for costly human annotation or dependence on proprietary models as judges.
- · AI research labs
- · Open-source AI developers
- · Industries with open-ended problems (e.g., creative content, complex problem-sol
- · Companies specializing in human data annotation for AI
- · AI models reliant solely on supervised learning for open-ended tasks
Self-play methods become more versatile and effective for general-purpose AI development.
Accelerated development of AI agents capable of handling complex, unstructured real-world problems.
Reduced entry barriers for developing sophisticated AI for niche open-ended applications, potentially fostering innovation outside major tech firms.
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