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Economic interactions among agents can spontaneously generate sophisticated reasoning strategies, outperforming traditional monolithic approaches.
Escaping the confines of autoregressive generation, Bidirectional Evolutionary Search unlocks significant performance gains by recombining partial trajectories and recursively decomposing tasks into checkable subgoals.
Recurrent Transformers let you trade model depth for width, slashing KV cache memory footprint and inference latency without sacrificing performance.
Language models can bootstrap their reasoning abilities without human labels by learning from each other's aggregated answers, achieving significant gains in mathematical reasoning.
