Researchers have introduced MORE, a novel self-evolving training framework designed to optimize reasoning in AI agents. This framework adaptively selects both the type of reasoning (direct answering, chain-of-thought, or code-based) and the computational effort required for each task. By learning these selections jointly, MORE allocates more concise reasoning to simpler tasks while reserving extended deliberation for complex ones. Experiments across coding, mathematics, and general reasoning benchmarks show that MORE consistently improves performance and efficiency, leading to higher accuracy with reduced response lengths and inference times. AI
IMPACT This framework could lead to more efficient and accurate AI agents across various complex tasks.
RANK_REASON The cluster describes a new research paper detailing a novel AI training framework. [lever_c_demoted from research: ic=1 ai=1.0]
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