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LLMs aren't always necessary: simple text embeddings in numerical bandits can match or beat LLM-based contextual bandits at a fraction of the cost.
Smaller language models can now achieve state-of-the-art claim verification performance by jointly optimizing for decomposition quality and verification accuracy using a novel reinforcement learning approach.
