I'm curious if there is measurable value in diversity of thought, and if there's diminishing returns on a single models thought pattern.
For example, compute X tokens with model A, then feed those into model B, etc. to get chain of thought through a diverse set of mdoels rather than chain of thought through a heterogeneous chain.
Humans seem to strongly believe echo chambers are bad. Are LLMs the same?
there are and this what you optimize for. ensemble learning has a long literature on this. you want models that have the most diverse pool of capabilities so they complement each other. in verifiable tasks or classification this is straightfroward but a bit tricker in open ended text gen or CoT. this is one of our core research question