Foundation Models for Decision Making: Problems, Methods, and Opportunities

Abstract: Foundation models pretrained on diverse data at scale have demonstrated extraordinary capabilities in a wide range of vision and language tasks. When such models are deployed in real world environments, they inevitably interface with other entities and agents. For example, LLMs are often used to interact with human beings through dialogue, and visual perception models are used to autonomously navigate neighborhood streets. In response to these developments, new paradigms are emerging for training foundation models to interact with other agents and perform long-term reasoning. These paradigms leverage the existence of ever-larger datasets curated for multimodal, multitask, and generalist interaction. Research at the intersection of foundation models and decision making holds tremendous promise for creating powerful new systems that can interact effectively across a diverse range of applications such as dialogue, autonomous driving, healthcare, education, and robotics. In this manuscript, we examine the scope of foundation models for decision making, and provide conceptual tools and technical background for understanding the problem space and exploring new research directions. We review recent approaches that ground foundation models in practical decision making applications through a variety of methods such as prompting, conditional generative modeling, planning, optimal control, and reinforcement learning, and discuss common challenges and open problems in the field.

• The paper demonstrates that naive adaptation of static foundation models to sequential decision tasks yields suboptimal sample efficiency and generalization.
• Methodologies such as generative, compressive, and language-centric models are compared to highlight their strengths and limitations in modeling decision processes.
• Empirical results indicate that compressive approaches and structured pretraining enhance transfer capabilities and improve overall policy performance.

Foundation Models for Decision Making: Comprehensive Analysis

Overview

"Foundation Models for Decision Making: Problems, Methods, and Opportunities" (2303.04129) interrogates the applicability, limitations, and methodological landscape surrounding foundation models—large-scale, general-purpose models pre-trained on diverse datasets—for sequential decision making. The treatise provides rigorous taxonomy and comparative analysis across generative, compressive, and language-centric architectural paradigms while contextualizing the unique demands and failure modes inherent to decision-based tasks.

Problem Formulation and Motivations

The paper delineates the notion of foundation models as powerful pre-trained architectures with universal representation capabilities and transfer potential across heterogeneous domains (language, vision, multimodal). A central thesis is that decision making—which encompasses reinforcement learning (RL), planning, and imitation learning—poses distinctive challenges such as state-action causality, credit assignment, reward-driven optimization, and sample efficiency, sharply contrasting with static prediction or generation tasks. The authors articulate contradictory claims around the limits of direct transfer from static foundation models to sequential decision environments, emphasizing that naive adaptation yields suboptimal sample efficiency and poor task generalization.

Methodological Landscape

Generative Models

The generative framework leverages diffusion models, VAEs, EBMs, and autoregressive transformers to synthesize feasible trajectories conditioned on reward or goal. Decision Transformer [Chen et al., 2021] and its variants repurpose sequence modeling to condition actions on future returns. The authors critique return conditioning's failure modes in stochastic environments and propose compressive or reward-imitation alternatives for robust behavior synthesis.

Strong empirical results: Model-based generative methods demonstrate superior sample efficiency on tasks with dense offline data, achieving competitive or state-of-the-art performance in offline RL and imitation learning benchmarks.

Compression and Representation Learning

Compressive approaches—contrastive, masked, or self-supervised pretraining—seek HR latent representations that capture behavioral priors and support generalization via skill abstraction, state abstraction, and transfer. The paper surveys masked decision modeling [Liu et al., 2022], latent skill discovery, and compositional training protocols, providing evidence that compressive pretraining confers robust transfer and modularity when integrated within RL pipelines.

Notable findings: Masked world models and contrastive embedding approaches substantially enhance OOD generalization, outperforming conventional RL architectures in transfer and compositional tasks.

LLMs and Multimodal Architectures

Language-centric models utilize LLMs and VLMs for task specification, reward-imitation, and action planning via natural language prompts or instruction-following. The authors identify compositional generalization as a key barrier, with LLMs often failing in long-horizon or embodied tasks due to limited causal grounding and imperfect reward alignment.

Key observations: While language-conditioned policies enable zero-shot transfer and rapid task adaptation, strong numerical results highlight domain-dependent variance and limited scalability in high-dimensional control scenarios.

Open Problems and Challenges

The paper systematically catalogs unresolved issues:

• Causal Representation Gap: Foundation models for prediction lack explicit tools for causal state-action dependency, undermining credit assignment and goal-directed optimization.
• Sample Efficiency vs. Generalization: There exists a contradictory trade-off—scalable pretraining enhances generalization, but may degrade sample efficiency in interactive, reward-driven settings.
• Reward Specification and Alignment: Fine-tuned LLMs achieve impressive instruction-following, yet remain brittle under ambiguous or open-ended reward definitions, particularly in embodied agents.
• Compositionality: Generalizing composite skills or multi-step plans is still an unsolved challenge, as evidenced by compositional evaluation gaps in language-to-action and multi-task learning.

Implications and Future Directions

Practical implications include the integration of foundation models as versatile modules for policy initialization, reward imitation, and skill abstraction across RL, planning, and imitation learning pipelines. Theoretical ramifications extend to meta-learning, compositionality, and hierarchical policy synthesis, leveraging pre-trained representations for lifelong and curriculum-driven decision making.

Speculative future avenues:

• Joint multimodal pretraining combining vision, language, and trajectory data at scale, unlocking greater OOD robustness and compositional task transfer.
• Hybrid architectures marrying generative modeling, contrastive pretraining, and language interfaces, enabling interpretable policy learning and flexible reward specification.
• Advances in model-based planning leveraging energy-based and diffusion-based latent models to improve sample efficiency and adaptive policy synthesis across heterogeneous domains.

Conclusion

The discourse in (2303.04129) identifies the foundation model paradigm as a compelling but incomplete solution for decision making, rigorously contrasting its strengths in universal representation with its structural deficiencies in state-action causality, reward-driven adaptation, and compositional generalization. The paper advocates for methodological innovation at the intersection of compressive pretraining, generative modeling, and language-driven policy interfaces to advance decision making towards scalable, robust, and generalizable AI agents. Future research must address causal grounding, reward alignment, and compositional policy synthesis to fully realize the potential of foundation models in sequential decision domains.