Research
114d agovLLM V0 to V1 RL Framework
The vLLM team introduced a new reinforcement learning framework prioritizing correctness before corrections. This approach ensures models master the core logic of a task before refining the output format. It prevents the common pitfall of reward hacking.
The Signal
Practitioners can now train models that prioritize factual accuracy over stylistic mimicry during the RL process.