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

Training AI reasoning models demands resources that most enterprise teams do not have. Engineering teams are often forced to choose between distilling knowledge from large, expensive models or relying on reinforcement learning techniques that provide sparse feedback.Researchers at JD.com and several academic institutions recently introduced a new training paradigm that sidesteps this dilemma. The technique, called Reinforcement Learning with Verifiable Rewards with Self-Distillation (RLSD), combines the reliable performance tracking of reinforcement learning with the granular feedback of self-distillation. Experiments indicate that models trained with RLSD outperform those built on classic distillation and reinforcement learning algorithms. For enterprise teams, this approach lowers the te