Google DeepMind has developed a new LLM reasoning technique called Self-Discover, which focuses on having the model design its own reasoning plan before solving a problem. Unlike generic methods like chain-of-thought, Self-Discover uses a fixed toolbox of 39 task-agnostic reasoning modules. It then selects relevant modules, adapts them to the specific task, and composes them into an ordered plan. This discovered plan is then used to solve the problem, with subsequent similar problems being solved more efficiently by simply filling in the plan's slots. This approach front-loads the reasoning cost, making it more efficient for tasks with many varied, reasoning-heavy instances. AI
IMPACT This technique could improve LLM accuracy on complex reasoning tasks by allowing models to tailor their approach to specific problems.
RANK_REASON The item describes a new LLM reasoning technique developed by a major AI lab. [lever_c_demoted from research: ic=1 ai=1.0]
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