The arXiv preprint introduces SynCo, a method for data synthesis co-training in self-evolving large language models. The authors frame self-evolving agents as a way to improve autonomously through continual interaction and learning, thereby reducing reliance on manually curated supervision.
The abstract states that realizing this promise requires not only updating the agent, but also… and then the available text cuts off. Based on the title, the proposed solution involves multi-agent reinforcement learning to coordinate data synthesis and training, but the released abstract does not specify the full architecture or experimental results.
Because this is a single preprint with a truncated abstract, this digest can only report the stated motivation and the proposed name. Readers seeking technical details should consult the full paper on arXiv.