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Sakana AI’s TreeQuest: Deploy multi-model teams that outperform individual LLMs by 30%
Sakana AI's new inference-time scaling technique uses Monte-Carlo Tree Search to orchestrate multiple LLMs to collaborate on complex tasks.
Another, simpler method is repeated sampling, where the model is given the same prompt multiple times to generate a variety of potential solutions, similar to a brainstorming session. “Our framework offers a smarter, more strategic version of Best-of-N (aka repeated sampling),” Takuya Akiba, research scientist at Sakana AI and co-author of the paper, told VentureBeat. “This demonstrates that Multi-LLM AB-MCTS can flexibly combine frontier models to solve previously unsolvable problems, pushing the limits of what is achievable by using LLMs as a collective intelligence,” the researchers write.
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