SynCo: Data Synthesis Co-Training for Self-Evolving LLMs via Multi-Agent Reinforcement Learning
To address this problem, we propose SynCo, an agentic data synthesis co-training framework for self-evolving LLMs based on multi-agent reinforcement learning.
Key points
- Self-evolving LLM agents promise to improve autonomously through continual interaction and learning, reducing their dependence on manually curated supervision.
- Realizing this promise requires not only updating the agent, but also evolving its training experience as its capabilities change.
- This growing mismatch between agent capability and training experience limits sustained self-improvement.
- SynCo jointly optimizes two independently parameterized agents: a Synthesizer that constructs training tasks from the Reasoner's evolving capability state, and a Reasoner that learns from the resulting experience.
Sources (1)
- [1]SynCo: Data Synthesis Co-Training for Self-Evolving LLMs via Multi-Agent Reinforcement LearningarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 8, 06:40 AM
To address this problem, we propose SynCo, an agentic data synthesis co-training framework for self-evolving LLMs based on multi-agent reinforcement learning.
Self-evolving LLM agents promise to improve autonomously through continual interaction and learning, reducing their dependence on manually curated supervision.
Extractive summary: sentences quoted from the sources.
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