Research · BAIR Berkeley ·

Teaching LLMs to Update Beliefs for Efficient Long-Horizon Interaction

Teaching LLMs to Update Beliefs for Efficient Long-Horizon Interaction

BAIR Berkeley introduces ABBEL, a framework for managing long-horizon LLM interactions with natural-language belief states instead of full conversation histories. It supervises and grades each belief state to improve the quality of compressed context over recursive summarization.

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