D$^2$Evo: Dual Difficulty-Aware Self-Evolution for Data-Efficient Reinforcement Learning

In three linesD²Evo is an RL framework to enhance LLM reasoning through self-evolution. The method generates medium-difficulty training samples by mining anchors matched to model capability, then jointly optimizes a Questioner and Solver. Results: outperforms existing methods on mathematical reasoning benchmarks with <2K real examples.

Summary generated by Claude — human-verified