Researchers at UC Berkeley, UIUC, and NYU developed an algorithmic framework that uses reinforcement learning (RL) to optimize vision and language models (VLM)

Using linguistic thinking, broad vision-language models (VLMs) have demonstrated remarkable capabilities as adaptive agents that can solve a wide range of tasks. A good way to improve VLM performance is to tune them with specific visual instruction tracking data. Their performance improves greatly with this strategy, which teaches them to obey precise visual instructions.

However, this method has disadvantages as it mainly relies on supervised learning from previously collected information. It may not be the ideal method for training agents in multi-step interactive environments that require language understanding in addition to visual recognition. The reason for this is that the diversity necessary to cover the wide range of decision-making scenarios that these agents may encounter may not be present in these previously collected data sets.

Reinforcement learning (RL) offers a way to overcome these constraints and fully develop the decision-making capabilities of VLM agents in complex multi-step situations. While reinforcement learning has been effective in training agents for a variety of text-based tasks, it has not yet been widely used to optimize vector language models (VLMs) for tasks that require end-to-end visual and language processing. other.

In recent research, a team of researchers has created an algorithmic framework that uses reinforcement learning to optimize VLMs and address this problem. First, the framework provides the task description to the VLM, which makes the model provide chain-of-thought (CoT) reasoning. This is an important stage because it allows the VLM to study intermediate steps in reasoning that logically lead to the final text-based action needed to complete the task.

The text output produced by the VLM is processed into executable actions so that the agent can communicate with its environment. The agent is rewarded through these interactions according to how well its actions achieve the job objectives. These rewards are then used to use RL to fine-tune the entire VLM, improving its decision-making ability.

Empirical testing findings have shown that this paradigm greatly improves the performance of VLM agents in decision-making tasks. For example, this approach allowed a 7 billion parameter model to outperform popular commercial models such as GPT-4V and Gemini. The team shared that they discovered that these performance advantages are only possible with the CoT reasoning component. The overall model performance decreased significantly when they evaluated this strategy without using CoT reasoning. This demonstrates the importance of CoT reasoning in the RL training framework and its crucial role in improving the decision-making capabilities of VLMs.

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