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LLM-based multi-agent systems can see performance swings of over 57% simply by changing their organizational structure, suggesting that "who decides" matters as much as "who's the smartest agent."
Pointwise reward models can finally compete with pairwise models in RLHF, thanks to a new intergroup comparison method that scales linearly with the number of candidates.
