GFlowNets learn their own generation order: four of five runs converge on checksum tasks where a uniform backward policy never does
GFlowNets build discrete objects one component at a time, and the order determines which conditional distributions the network must learn. Previously the order was fixed by a preset or uniform backward policy, and standard training struggled to find useful orders. Learning-Order GFlowNet learns positions and symbols jointly without order labels, converging in four of five runs on checksum dependencies, against zero for a uniform backward policy and two of ten for fixed random orders.