TRL · v1.8.0
KTO trainer graduates from experimental to the top-level trl package with the same API as DPO/GRPO/SFT, and the experimental import path still works with a FutureWarning. Environment-owned rewards let agentic RL environments define their own reward via a reserved get_reward() method, and multi-environment support allows a single training run to handle multiple environments with environment-specific tool schemas. GRPO now supports both static and adaptive entropy regularization to encourage exploration and prevent policy collapse.