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LLMs can be made to reason much better by directly optimizing their pre-training output distribution, even before fine-tuning on specific tasks.
LLM agents can learn to continuously adapt and improve in complex environments by reflecting on past experiences and explicitly storing/retrieving reusable lessons, leading to substantial performance gains.
