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LLM agents can navigate long-horizon tasks far more effectively by dynamically compressing their interaction history based on a learned context budget, outperforming fixed-strategy baselines by up to 60% in complex scenarios.
Forget fine-tuning: merging models in weight space lets you adapt LLMs to new domains without sacrificing instruction-following ability, even with limited data.
