Teaching LLMs to Update Beliefs for Efficient Long-Horizon Interaction
BAIR
Researchers developed ABBEL, a framework that teaches language models to maintain and update natural-language belief states instead of storing full interaction history, improving performance on long-horizon tasks like collaborative coding. On CollabBench, ABBEL with reconstruction-based belief grading reduced the performance gap versus full-context models by 50% while requiring 50% fewer training steps and using 6,000 context tokens versus 14,000. This enables LLMs to handle extended interactions more efficiently while preserving task performance where data for training summarization is limited.
Why it matters
.abbel-fig { display: block; text-align: center; margin: 2.4em 0; line-height: 1.4; max-width: 100%; } .abbel-fig img { display: block; margin: 0.65em auto 0; height: auto; max-width: 100%; } /* Image sizes; captions use a narrower measure below */ .abbel-fig--wide img { width: 100%; max-width: 100%; } .abbel-fig--wide-90 img { width: 100%; max-width: 90%; } .abbel-fig--wide-lg img { width: 100%; max-width: 100%; } .abbel-fig--chart img { width: 100%; max-width: 82%; } .abbel-fig--chart-sm img { width: 100%; max-width: 64%; } .abbel-fig--portrait img { width: 50%; max-width: 520px; } .abbel-fig--equation img { width: 100%; max-width: 52%; } .abbel-fig--video { width: 110%; max-width: 110%; margin-left: -5%; margin-right: -5%; box-sizing: border-box; } .abbel-fig--video .abbel-frames { max-width: 100%; width: 100%; } .abbel-frames { margin: 0.65em auto 0; max-width: 100%; user-select: none; } .abbel-frames__stage { position: relative; cursor: pointer; border: none; background: transpare
