Retrospective Trajectory Optimization Via Policy Gradient Learning

via “generative-reward-optimization-grpo-training”

Train transformer language models with reinforcement learning.

Unique: Implements unified reward+policy training where the model generates both outputs and rewards in a single forward pass, reducing pipeline complexity compared to RLHF while maintaining explicit reward signals through a learned reward head

vs others: More integrated than RLHF because it eliminates separate reward model training, while more explicit than DPO because it maintains interpretable reward scores that can be inspected and debugged