Beginner Explanation
Imagine you have a friend who is really good at drawing pictures and another friend who is great at telling stories. If they work together, your drawing friend can create illustrations based on the story your storytelling friend tells. Now, if you give them both feedback on how well they did, they can improve and create even better illustrations and stories together. Joint-GRPO is like that teamwork, where a model that understands images and a model that understands videos work together, sharing feedback to make their results even better.Technical Explanation
Joint-GRPO (Joint Generative Reward Policy Optimization) is a framework that integrates a Vision-Language Model (VLM) and a Video Diffusion Model (VDM) to enhance their outputs through collaborative learning. The architecture allows these models to share a common reward signal, which is computed based on their combined performance on tasks like video generation or captioning. For instance, the VLM generates textual descriptions of video frames while the VDM creates video sequences. The reward function evaluates the coherence and relevance of the generated outputs, guiding both models in their training. In Python, you might implement a simple version using a reinforcement learning library such as Stable Baselines to optimize the policy based on shared rewards: “`python from stable_baselines3 import PPO # Define your models and environment vlm = VisionLanguageModel() vdm = VideoDiffusionModel() # Training loop for episode in range(num_episodes): state = env.reset() done = False while not done: action = vlm.predict(state) next_state, reward, done, info = env.step(action) vdm.update(state, action, reward) state = next_state “` This code snippet illustrates a basic training loop where both models learn from the environment based on shared rewards.Academic Context
Joint-GRPO is situated at the intersection of reinforcement learning, computer vision, and natural language processing. The foundational concepts include policy optimization and reward shaping, which are critical for effective multi-agent collaboration. Notable works include ‘Deep Reinforcement Learning with Double Q-learning’ (van Hasselt et al., 2016) and ‘Attention is All You Need’ (Vaswani et al., 2017), which underpin the architectures of VLMs and VDMs. The mathematical formulation involves defining a joint policy π(a|s) that maximizes the expected cumulative reward E[R] over time, where R is derived from the outputs of both models. Research continues to evolve in this area, exploring how shared rewards can enhance generative tasks in multimodal systems.Code Examples
Example 1:
from stable_baselines3 import PPO
# Define your models and environment
vlm = VisionLanguageModel()
vdm = VideoDiffusionModel()
# Training loop
for episode in range(num_episodes):
state = env.reset()
done = False
while not done:
action = vlm.predict(state)
next_state, reward, done, info = env.step(action)
vdm.update(state, action, reward)
state = next_stateExample 2:
state = env.reset()
done = False
while not done:
action = vlm.predict(state)
next_state, reward, done, info = env.step(action)
vdm.update(state, action, reward)
state = next_stateExample 3:
from stable_baselines3 import PPO
# Define your models and environment
vlm = VisionLanguageModel()
vdm = VideoDiffusionModel()View Source: https://arxiv.org/abs/2511.16669v1