Teaching LLMs to Update Beliefs for Efficient Long-Horizon Interaction

The post introduces ABBEL, a framework that replaces full interaction history with periodically updated natural-language "belief states" and supervises their contents through belief grading, including an autoencoding-inspired reconstruction objective. On CollabBench collaborative coding, reconstruction-based belief grading roughly halved the gap to full-context models and trained in 50 steps instead of 100, while using fewer peak tokens; on Combination Lock and multi-objective QA, domain-knowledge grading and a peak belief length penalty further improved efficiency. The authors argue that isolating and supervising summaries addresses the performance cost of recursive summarization in long-horizon tasks.