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🧠 AI🟢 BullishImportance 7/10
SAGE: Multi-Agent Self-Evolution for LLM Reasoning
arXiv – CS AI|Yulin Peng, Xinxin Zhu, Chenxing Wei, Nianbo Zeng, Leilei Wang, Ying Tiffany He, F. Richard Yu|
🤖AI Summary
Researchers introduced SAGE, a multi-agent framework that improves large language model reasoning through self-evolution using four specialized agents. The system achieved significant performance gains on coding and mathematics benchmarks without requiring large human-labeled datasets.
Key Takeaways
- →SAGE uses four co-evolving agents (Challenger, Planner, Solver, Critic) to improve LLM reasoning capabilities through self-play.
- →The framework reduces dependency on large human-labeled datasets by using only a small seed set for training.
- →SAGE improved Qwen-2.5-7B model performance by 8.9% on LiveCodeBench and 10.7% on OlympiadBench.
- →The Critic agent prevents curriculum drift and maintains training quality through scoring and filtering mechanisms.
- →The approach shows consistent gains across different model scales in mathematics and code generation tasks.
#sage#multi-agent#llm#reasoning#self-evolution#machine-learning#code-generation#mathematics#qwen#arxiv
Read Original →via arXiv – CS AI
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