Learning, Fast and Slow: Towards LLMs That Adapt Continually — mentioned by LakshyAAAgrawal

Wed, 13 May 2026 17:08:35 GMT

Large language models (LLMs) are trained for downstream tasks by updating their parameters (e.g., via RL). However, updating parameters forces them to absorb task-specific information, which can result in catastrophic forgetting and loss of plasticity. In contrast, in-context learning with fixed LLM parameters can cheaply and rapidly adapt to task-specific requirements (e.g., prompt optimization), but cannot by itself typically match the performance gains available through updating LLM parameters. There is no good reason for restricting learning to being in-context or in-weights. Moreover…

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Learning, Fast and Slow: Towards LLMs That Adapt Continually · HackerPapers discussions

Learning, Fast and Slow: Towards LLMs That Adapt Continually — mentioned by LakshyAAAgrawal