ABBEL: LLM Agents Acting through Belief Bottlenecks Expressed in Language
Analysis
This ArXiv paper introduces ABBEL, a framework for LLM agents to maintain concise contexts in sequential decision-making tasks. It addresses the computational impracticality of keeping full interaction histories by using a belief state, a natural language summary of task-relevant unknowns. The agent updates its belief at each step and acts based on the posterior belief. While ABBEL offers interpretable beliefs and constant memory usage, it's prone to error propagation. The authors propose using reinforcement learning to improve belief generation and action, experimenting with belief grading and length penalties. The research highlights a trade-off between memory efficiency and potential performance degradation due to belief updating errors, suggesting RL as a promising solution.
Key Takeaways
- •ABBEL framework allows LLM agents to maintain concise contexts using belief states.
- •Belief bottlenecks can lead to error propagation, impacting performance.
- •Reinforcement learning can be used to improve belief generation and mitigate error propagation.
“ABBEL replaces long multi-step interaction history by a belief state, i.e., a natural language summary of what has been discovered about task-relevant unknowns.”