A novel training paradigm that combines reinforcement learning with self-distillation is making it easier and cheaper to build custom reasoning agents. The approach, called RLSD, reduces the computational resources needed while improving model performance, according to a recent announcement on the AI research channel The AI Opus.
Key Highlights:
- RLSD lowers barriers to developing reasoning models.
- The method offers efficiency gains and better results.
- It aims to democratize access to advanced AI reasoning.
The development could enable more teams to create specialized reasoning systems without requiring massive compute budgets, accelerating innovation in AI applications.