SynCo: Data Synthesis Co-Training for Self-Evolving LLMs via Multi-Agent Reinforcement Learning

Key points

SynCo is a data synthesis co-training framework that jointly optimizes an independently parameterized Synthesizer and Reasoner via multi-agent reinforcement learning.
The Synthesizer constructs training tasks from the Reasoner's evolving capabilities, resolving the capability-experience mismatch of static datasets.
On mathematical reasoning SYNCO reached 78.72%, outperforming the strongest external baseline by 3.76 points and the frozen synthesizer by 2.87.
As training progressed, the share of gradient-bearing tasks fell from 31.3% to 9.5% while all-correct tasks rose from 34.4% to 59.9%.
Only 16.2% of 2,940 verified tasks gave non-degenerate feedback, and teachability tracked this property at r = 0.934 while novelty was irrelevant at r = -0.041.

External research summaries. These are not HDATF publications or measured product results.