Research
114d agovLLM Prioritizes Correctness in Reinforcement Learning
The vLLM team shifted their RL approach to prioritize raw correctness over superficial formatting. By focusing on accuracy before applying corrections, they reduce the risk of reward hacking. This methodology prevents models from gaming the reward function.
The Signal
Practitioners can now implement more stable training loops for complex reasoning tasks.