Intern-Decision: Efficient Multimodal Decision Making with Autoregressive Masked Language Modeling
Interactive agents repeatedly choose among a finite set of actions, labels, or ordinal outcomes. Generating a free-form response for each choice adds latency and complicates the interface between a model and its environment. We present Intern-Decision, a family of 0.8B, 2B, and 4B multimodal decision models that return structured choices and candidate probabilities from text an
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Interactive agents repeatedly choose among a finite set of actions, labels, or ordinal outcomes. Generating a free-form response for each choice adds latency and complicates the interface between a model and its environment. We present Intern-Decision, a family of 0.8B, 2B, and 4B multimodal decision models that return structured choices and candidate probabilities from text an
This work introduces an efficient approach for multimodal decision-making, crucial for interactive AI agents. By generating structured choices directly, it reduces latency and simplifies model-environment interaction, which is vital for real-time applications.