Synthesizing Cross-Modal Decision Policies through Reinforcement Learning Integrating Visual Perception and Large Language Model Tactical Planning
DOI:
https://doi.org/10.66280/cis.v1i1.149Abstract
The convergence of high-dimensional visual perception and high-level linguistic reasoning represents a frontier in autonomous systems research, particularly concerning the synthesis of robust decision policies. This paper explores the integration of visual sensory inputs with the tactical planning capabilities of Large Language Models (LLMs) within a Reinforcement Learning (RL) framework. While traditional RL excels at low-level motor control and reactive behaviors, it often lacks the semantic depth required for long-horizon strategic navigation in complex, semi-structured environments. Conversely, LLMs provide sophisticated world models and common-sense reasoning but remain fundamentally ungrounded without direct sensory alignment. Our research investigates a hybrid architectural approach where LLMs serve as tactical orchestrators that interpret environmental states conveyed through vision-language encoders, subsequently shaping the reward functions and action spaces for RL agents. We analyze the structural trade-offs inherent in this cross-modal synthesis, focusing on the latency of inference, the stability of the learned policies, and the alignment between symbolic reasoning and physical execution. Beyond the technical mechanics, the study delves into the socio-technical implications of such systems, including their governance, the transparency of cross-modal decision-making, and the long-term sustainability of deploying massive transformer-based models in edge-computing infrastructures. By evaluating these systems through the lens of robustness and fairness, we provide a comprehensive framework for understanding how hybrid cognitive architectures can be scaled responsibly. The findings suggest that while cross-modal integration significantly enhances task generalization, it introduces novel failure modes necessitated by the stochastic nature of language-based planning, requiring new paradigms for safety-critical deployment.
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