arXiv:2606.09052v1 Announce Type: new Abstract: Self-evolution offers a scalable path to stronger reasoning: a pretrained language model improves itself with only minimal external supervision. Yet existing methods either depend on extensively curated or teacher-generated training data, or, when the generator runs unsupervised, reward it by a difficulty heuristic that need not improve the solver. We introduce INFUSER, an iterative co-training framework with two co-evolving roles: a Generator that drafts questions and reference golden answers from a pool of unstructured, automatically collected
The rapid advancement in language models is pushing research towards autonomous self-improvement to overcome the limitations of supervised learning and extensive data curation.
This research outlines a method for language models to iteratively improve their reasoning capabilities with minimal external supervision, accelerating AI development and reducing reliance on human-curated datasets.
The paradigm for developing advanced reasoning models could shift from heavily supervised training to more autonomous, self-evolving systems, democratizing access to powerful AI capabilities.
- · AI developers
- · Companies with limited proprietary datasets
- · AI-powered services
- · Companies reliant on expensive curated datasets
- · Traditional supervised learning approaches
More sophisticated and robust AI models capable of complex reasoning will emerge.
The cost and time required to develop cutting-edge AI could significantly decrease, leading to broader adoption.
This could accelerate the development of general artificial intelligence by providing a more efficient path to higher reasoning capabilities.
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Read at arXiv cs.LG