The authors support ABBEL, reporting that its general reconstruction-based belief grader reduces the performance gap with full-context models by about 50% and significantly lowers memory usage.
Jakob Bjorner, Aly Lidayan, Satvik Golechha, Kartik Goyal, Alane Suhr ·
Supporting evidence
Teaching LLMs to Update Beliefs for Efficient Long-Horizon Interaction
Original excerpt
We see that with the general reconstruction-based belief grading function we reduce the performance gap from full context models by about 50%, and train in 50% fewer steps compared to training models to summarize without belief grading (no BG). After training, ABBEL still uses significantly less memory than the full context setting, as measured by the peak context token length (Peak Tokens).
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The object-specific interpretation and Chinese translation were checked independently against the source. This is an AI semantic review, not playback verification. Reviewed Oct 5, 2026 · qwen3.8-max-0902
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