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By recursively aggregating reasoning chains, even smaller LLMs can now achieve performance competitive with much larger models, challenging the assumption that scale is the only path to improved reasoning.
Forget Bayesian bells and whistles: in-context learning shines brightest with simple point estimators, outperforming complex posterior approximations in most scenarios.
