Multimodal Policy Internalization (MPI)

• Multimodal Policy Internalization (MPI) is a method that embeds policies from vision, language, and other modalities directly into model parameters, eliminating the need for explicit instructions during inference.
• MPI utilizes latent mode conditioning, categorical sampling, and diffusion models to capture diverse behavioral strategies, thereby enhancing both exploration and convergence in various tasks.
• MPI improves safety, efficiency, and adaptability in applications such as robotics, autonomous navigation, and conversational agents by ensuring robust adherence to complex, multimodal policies.

Multimodal Policy Internalization (MPI) refers to the process by which policies governing behaviors, decision rules, or control strategies across multiple information sources or modalities (e.g., vision, language, proprioception, or discrete behavioral modes) are embedded directly into agent or model parameters. MPI eliminates the need to provide explicit policy instructions at inference, thus enabling agents to consistently, robustly, and efficiently adhere to complex policies under diverse operational contexts. Recent advances span reinforcement learning (RL), model predictive control (MPC), vision-language alignment, and LLM-driven agentic workflows. MPI research addresses challenges arising from multimodal decision-making, sparse and complex reward signals, lengthy policy documents, and the requirement for grounded, reasoning-intensive policy adherence.

1. Theoretical Foundations and Definitions

MPI is motivated by the recognition that single-mode or unimodal policies (e.g., Gaussian in RL, fixed template prompts for LLMs) constrain agents to narrow behavioral repertoires and restrict adaptability. Formally, MPI seeks to learn a parameterization $\pi_\theta(\cdot)$ or model weights $\theta$ such that the agent’s responses or actions reflect the underlying multimodal policy $\mathcal{P}$ without requiring $\mathcal{P}$ at inference: $$\hat{a} = \pi_\theta(o) \approx \pi^*_\mathcal{P}(o),\ \forall\ o$$ where $o$ denotes multimodal input (observations, queries, images, etc.), and $\pi^*_\mathcal{P}$ is the policy-conditioned optimal response. Internalization can apply to physical control (e.g., bipedal locomotion), trajectory optimization, conversational agents, and reasoning over visual or textual contexts.

Two core paradigms emerge:

• Latent behavior-mode conditioning: Policies are conditioned on learned or discrete latent variables (e.g., mode vectors, categorical distributions, embeddings), enabling the representation of diverse behavioral strategies (Krishna et al., 2023, Islam et al., 19 Aug 2025).
• Policy document absorption: Formal policy descriptions (textual, tabular, visual) are injected into model pretraining or fine-tuning, supporting direct recall and application via learned model priors (Wang et al., 10 Oct 2025, Liu et al., 13 Oct 2025).

2. Algorithmic Mechanisms and Architectures

Several algorithmic frameworks operationalize MPI across settings:

Approach	Mechanism	Paper (arXiv id)
Latent-conditioned RL policy	Autoencoder for latent modes; policy $\pi(a|s,z)$	(Krishna et al., 2023)
Categorical Policies	Discrete mode selection via categorical latent & STE/Gumbel-Softmax sampling	(Islam et al., 19 Aug 2025)
Diffusion Policies	Multimodal action generation through diffusion models (DDiffPG)	(Li et al., 2 Jun 2024)
Model-based RL (RPG)	Latent trajectory variable $z$ & ELBO-based objective	(Huang et al., 2023)
LMPC for multimodal systems	Local affine time-varying models; data-driven safe set	(Kopp et al., 8 Jul 2024)
Policy Document Internalization (LLM)	Category-aware continued pretraining, targeted synthesis	(Liu et al., 13 Oct 2025)
Conversational MPI	Three-stage (TriMPI): continual pretraining, CoT SFT, RL PolicyRollout	(Wang et al., 10 Oct 2025)
Multimodal Safety Alignment	Chain-of-thought reasoning ground in policy, fine-grained data curation	(Xia et al., 24 Jun 2025)

Latent-mode approaches either use autoencoders (compressed behavioral trajectories into latent vectors, as in bipedal locomotion), categorical latent variables representing a combinatorial space of behavioral modes, or diffusion models to create flexible multimodal action paths. Policy document internalization proceeds by parsing and categorizing the key rules, synthesizing targeted pretraining examples, and minimizing the autoregressive loss across categorized segments (factual, behavioral, conditional).

In reinforcement learning, ELBO-based or maximum entropy objectives enforce both reward maximization and sufficient coverage of multiple behavioral modes, often via entropy bonuses, cross-entropy regularizers, or intrinsic motivation (e.g., object-centric RND). Mode-specific Q-learning and multimodal batching (DDiffPG) mitigate greedy optimization pitfalls that lead to mode collapse.

3. Data Representation, Encoding, and Training Techniques

MPI relies on tailored representation and sampling strategies:

• Latent vectors and mode embeddings: Autoencoded trajectory representations (Krishna et al., 2023), categorical one-hot or combinatorial latent codes (Islam et al., 19 Aug 2025), diffusion policy embeddings (Li et al., 2 Jun 2024), and continuous relaxation via Gumbel-Softmax provide differentiable links between discovered behavioral modes and action outputs.
• Adaptive sampling: Training leverages performance history (recent returns) to bias sampling towards under-represented modes, reducing aliasing and collapse (Krishna et al., 2023).
• Policy-grounded chain-of-thought (CoT) supervision: For VLMs and agentic LLM systems, reasoning traces reference explicit policy rules, intermediate visual/text grounding, and justification steps, ensuring robust safety and policy adherence (Xia et al., 24 Jun 2025).
• Multimodal batch construction and Q-learning: Clustering of behavioral trajectories and multimodal batches promote stable updates across all discovered modes (Li et al., 2 Jun 2024).
• Category-aware policy document parsing: Automatic categorization drives targeted data synthesis for continued pretraining, reducing manual annotation and enhancing learned policy recall (Liu et al., 13 Oct 2025).
• Continual pretraining and loss masking: Visual token masking and selective loss application ensure multimodal policies are internalized into the text-processing pathway (Wang et al., 10 Oct 2025).
• Sampled safe set in MPC: Local convex safe sets built from historical multi-modal data provide both feasibility and safety guarantees for decision tasks under unknown current modes (Kopp et al., 8 Jul 2024).

4. Empirical Validation and Performance Analysis

Empirical studies demonstrate superior performance of MPI-enabled methods in diverse domains:

Task / Benchmark	Performance Gain	Paper (arXiv id)
Bipedal parkour (gaps, plateaus, blocks)	~0.87 normalized returns (adaptive sampling), smooth transitions	(Krishna et al., 2023)
RL continuous control (DeepMind Suite)	Faster convergence, higher reward, reduced variance vs. Gaussian	(Islam et al., 19 Aug 2025)
AntMaze navigation / manip. tasks	Multimodal exploration, dynamic replanning, mode-specific success	(Li et al., 2 Jun 2024)
RL trajectory optimization (dense/sparse rewards)	Outperforms SAC, MBSAC, higher sample efficiency	(Huang et al., 2023)
MPC autonomous driving (friction variation)	Maintains constraints, robust convergence, faster adaptation	(Kopp et al., 8 Jul 2024)
Vision-language safety alignment	0.9888 safety rate, >30pt improvement, general reasoning preserved	(Xia et al., 24 Jun 2025)
Conversational agents (synthetic & real policy tasks)	70.7–79.4% accuracy gain, 93.9% token reduction, robust override	(Wang et al., 10 Oct 2025)
LLM agentic policy workflows	41%+ improvement over SFT baselines, 97.3% prompt compression	(Liu et al., 13 Oct 2025)

MPI strategies consistently enhance exploration, adaptation, and policy adherence. Adaptive sampling and latent-mode conditioning prevent mode collapse in RL. Structured multimodal policies are shown to converge faster and avoid local minima. In conversational systems, policy internalization yields large reductions in prompt length and inference latency, with increased robustness to policy updates and overrides.

5. Safety, Robustness, and Alignment Implications

Safety-critical reasoning is an increasingly prominent application domain for MPI. Vision-LLMs (VLMs) are vulnerable to multimodal jailbreaks, spurious visual grounding, and stepwise rationalization leading to unsafe outputs. MSR-Align introduces policy-grounded chain-of-thought supervision, enforces rule referencing for each reasoning step, and achieves high safety rates while preserving general reasoning skill (Xia et al., 24 Jun 2025).

In agentic LLM systems, internalized policies via CAP-CPT ameliorate the reasoning burden for complex, multi-level workflows, supporting scalable deployment in high-stakes business, regulatory, or tool-use environments (Liu et al., 13 Oct 2025). TriMPI's PolicyRollout mechanism directly augments exploration with policy-aware rollouts, increasing behavioral diversity and robustness.

Safe set sampling in MPC ensures feasible operation across unobserved or abruptly switched modes. Intrinsic motivation and mode-specific Q-learning reduce risks associated with policy collapse.

6. Limitations, Open Problems, and Future Directions

MPI methods face several outstanding challenges:

• Scalability: Latent mode dimensionality (autoencoders, combinatorial categorical variables) must balance expressiveness and tractable optimization (Islam et al., 19 Aug 2025).
• Generalization: Internalized policies must remain robust under significant policy overrides, cross-domain transfer, or mixtures of task response formats (Wang et al., 10 Oct 2025).
• Reasoning Depth and Data Burden: Performance drops sharply with increasing workflow complexity. Automated data synthesis and categorization are essential to reduce annotation effort (Liu et al., 13 Oct 2025).
• Alignment in Dynamic, Adversarial Scenarios: Continuous improvement in policy-grounded reasoning pipelines and RLHF are required to address evolving policy threats and compositional prompt attacks (Xia et al., 24 Jun 2025).
• Hardware Transfer: Reward shaping and exteroceptive feedback integration are open areas for transferring simulation-trained MPI controllers to real-world platforms (Krishna et al., 2023).
• Dynamic Mode Selection and Online Replanning: Enabling rapid switching and adaptation to previously unseen contexts remains an active research direction (Li et al., 2 Jun 2024).

A plausible implication is the accelerating integration of MPI with scalable, reliable, and explainable multimodal agents and controllers, spanning control robotics, dialog systems, safety regulation, and autonomous workflow execution. The discipline will continue to refine representation, sampling, and policy-absorbing architectures to advance generality and efficiency, while handling complexity in both physical and reasoning domains.