Self-Adapting Language Models: Paper Authors Discuss Implications
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Challenges in Self-Adapting Language Models
This chapter explores the complexities of self-adapting language models, focusing on issues like catastrophic forgetting and gradient interference. It discusses methodologies such as LoRa for weight updates and emphasizes the inefficiencies in reinforcement learning that necessitate further research.
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