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Answer-conditioning can undermine reasoning accuracy in LLMs, leading to a staggering 27-point drop on difficult tasks.
Assessment-free isolation can match the performance of full multi-agent assessment, revealing a surprising efficiency in weaker models.
DART boosts reasoning accuracy by up to 22.5 points while slashing thinking token usage by over 50%, all without requiring labeled training data.
ACOER reduces token generation by over 60% while boosting accuracy, solving the reward collapse problem that plagues traditional efficiency training methods.
Multilingual retrievers often prioritize irrelevant English documents over relevant foreign-language documents, even when the query is in that foreign language.
