XferAgent: Cross-Agent Experience Transfer for Self-Improving LLM Agents
We propose XferAgent, a framework for cross-agent experience transfer that organizes reusable experience in local and shared experience graphs.
ProofPaper ↗
Key points
- Large language model (LLM) agents can reuse experience from prior task executions to improve performance on subsequent tasks.
- Although existing methods enable cross-agent experience sharing, differences in agents' execution histories and task requirements make shared experiences difficult to reuse effectively.
- XferAgent reconciles compatible contributions from heterogeneous agents while avoiding unreliable merges.
- Experiments across diverse task domains show improvements over representative memory-based methods and effective experience transfer between heterogeneous agent frameworks.
Sources (1)
- [1]XferAgent: Cross-Agent Experience Transfer for Self-Improving LLM AgentsarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 14, 04:51 AM
We propose XferAgent, a framework for cross-agent experience transfer that organizes reusable experience in local and shared experience graphs.
Large language model (LLM) agents can reuse experience from prior task executions to improve performance on subsequent tasks.
Extractive summary: sentences quoted from the sources.
Before this
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