How to build custom reasoning agents with a fraction of the compute

Researchers from JD.com and academic partners proposed RLSD, a new training method for reasoning models.

RLSD combines reinforcement learning with verifiable rewards and self-distillation to address limits of standard RL, on-policy distillation, and self-distillation.

In experiments, RLSD outperformed classic distillation and reinforcement-learning approaches while lowering training cost and complexity.

The method could make it easier and cheaper for enterprises to train domain-specific AI reasoning systems without a separate teacher model or massive GPU budgets.