# reinforcement learning post-training

RoboFFT, from the PKU-Lingchu joint lab and accepted at CoRL2026, turns the forward noising loss already used to pretrain diffusion and flow policies into an approximate probability ratio, so generative robot policies can be post-trained PPO-style from task success or failure feedback. It was tested on Robomimic simulation tasks, the flow action head of vision-language-action model π₀, and four real-robot manipulation tasks; on eggplant grasping at the same data budget, behavior cloning on pooled data reached 14/20, offline post-training 18/20 and online post-training 20/20.

RoboFFT, from the PKU-Lingchu joint lab and accepted at CoRL2026, turns the forward noising loss already used to pretrain diffusion and flow policies into an approximate probability ratio, so generative robot policies can be post-trained PPO-style from task success or failure feedback. It was tested on Robomimic simulation tasks, the flow action head of vision-language-action model π₀, and four real-robot manipulation tasks; on eggplant grasping at the same data budget, behavior cloning on pooled data reached 14/20, offline post-training 18/20 and online post-training 20/20.