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By focusing on correcting "near-miss" answers, REVES achieves a remarkable +6.5 point improvement over standard RL methods, showcasing a new way to enhance LLM reasoning without extensive computational costs.
LLM agents can learn complex, multi-turn tasks far more effectively by explicitly separating planning from execution, using a hierarchical RL approach with carefully designed credit assignment.
