Get the latest tech news
DeepMind’s new inference-time scaling technique improves planning accuracy in LLMs
With "Mind Evolution" LLMs can use search and genetic algorithms to generate and combine different solutions and find the optimal one.
In its latest research paper, Google DeepMind introduced the concept of “ Mind Evolution,” a technique that optimizes responses of large language models (LLMs) for planning and reasoning tasks. For example, Gemini 1.5 Flash and o1-preview achieve a success rate of only 5.6% and 11.7% on TravelPlanner, a benchmark that simulates organizing a trip plan based on user preferences and constraints expressed in natural language. On the Trip Planning benchmark, which involves creating an itinerary of cities to visit with a number of days in each, Mind Evolution achieved 94.1% on the test instances while other methods reached a maximum of 77% success rate.
Or read this on Venture Beat