ABBEL: Teaching LLMs to Update Belief States for Long-Horizon Interaction

Berkeley AI Research introduces ABBEL, a framework that replaces full interaction history with natural-language belief states and supervises their contents via belief grading. On CollabBench collaborative coding, reconstruction-based belief grading cuts the gap to full-context models by about 50% and trains in 50 steps instead of 100, while using fewer peak tokens.