Technical Talks

• Model Systems

Reinforcement Learning from Verifiable Rewards (RLVR) is increasingly common in post-training pipelines, but the practical details are often glossed over. How do you design reward functions that programmatically verify model outputs? What makes synthetic training data effective? How do you build a custom RL environment that doesn't silently break your training?